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Working knowledge of trust and safety analytics metrics (fraud signals, bad actor patterns, etc.) Strong understanding of incrementally as well as sizing business problems and opportunities Understanding of basic statistical concepts such as confidence interval, normal distribution, correlation, linear regression, and multivariate testing Strong communication skills at different levels of organizational hierarchy Proactive skill set that encompasses problem identification, analysis, solution definition, results, and communication Proficiency with SQL, R and Python (or equivalent) In depth experience with data visualization and analytical tools (E.g. 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Bachelor's degree in Computer Science, Engineering, Business, or an equivalent; MBA or advanced degree preferred. 5+ years of experience in product management, focusing on trust and safety, integrity, authentication, or related areas. Proven track record of leading successful product initiatives in a fast-paced, dynamic environment. Strong analytical ability to demonstrate data to drive product decisions. Excellent communication and social skills, with the ability to influence partners at all levels of the organization. Deep empathy for users and a passion for creating safe and inclusive online communities. Experience working with cross-functional teams in an agile development environment. 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Collaborate with Match Group properties across the globe to align internal and external partners on strategies that maximize the impact and efficiency of product & moderation teams while solving underlying trust & safety concerns. Develop metrics to measure the success of service changes, analyze results, and use analysis to inform further iteration. Manage data accessibility for reporting purposes, designing dashboards to communicate KPIs to key internal and external stakeholders. Provide recommendations for new technologies; support third-party vendor communications, evaluations, and integrations. 5+ years of product management, developer relations, software development or related experience in an agile environment, preferably with Trust & Safety or related experience. Domain knowledge in Trust & Safety, Identity, Fraud Detection and Prevention Experience supporting business-critical systems for multinational customers at scale in a B2B environment. Ability to qualify how the API/service delivers value from a product perspective. Experience working with large datasets for planning and analysis, experience with analytics tools such as SQL and BI software Expert working knowledge of Google Suite, Jira, Confluence, Slack Ability to structure problem-solving approaches and bring multiple solutions to solving complex problems. Experience developing processes for sharing information and insights across large organizations. Experience working with third party technology providers. Equal comfort communicating to business stakeholders and engineers; ability to tailor the message appropriately to the audience. Ease with building trust with cross-functional stakeholders through integrity, honesty, authenticity, and drive.", "stars": 0.0, "medallionProgram": null, "location_flexibility": null, "work_location_option": "onsite", "canonicalPositionUrl": "https://join.matchgroupcareers.com/careers/job/21461522", "isPrivate": false}, {"id": 22260162, "name": "Engineering Manager II, Backend", "location": "West Hollywood, California", "locations": ["West Hollywood, California"], "hot": 0, "department": "Tinder", "business_unit": "Engineering", "t_update": 1714762653, "t_create": 1714762653, "ats_job_id": "c94ecbe9-1367-456b-b4b2-0bf987b1f08b", "display_job_id": "c94ecbe9-1367-456b-b4b2-0bf987b1f08b", "type": "ATS", "id_locale": "986852860409117513-en", "job_description": "Define and own platform objectives and key metrics, developing a strategic roadmap for your team. Collaborate with h cross-functional/platform teams to execute the roadmap, delivering high-quality results promptly Drive technical, design and product discussions with teams across the organization to define and scope requirements. Establish the technical direction for the team and lead the team to achieve it. Mentor and coach your team for career development. Recruit and assemble a world-class backend team. 7+ years of professional experience building scalable backend services. 2+ year(s) of experience leading backend engineering teams. Significant experience with Java and/or Scala, Node.js, Typescript. Cloud platform experience: AWS, GCP or Azure Proven experience leading a team in building and shipping scalable distributed backend systems. Excellent communication skills with the ability to collaborate effectively with cross-functional teams. Ability to inspire and motivate the team to exceed expectations.", "stars": 0.0, "medallionProgram": null, "location_flexibility": null, "work_location_option": "onsite", "canonicalPositionUrl": "https://join.matchgroupcareers.com/careers/job/22260162", "isPrivate": false}, {"id": 17634306, "name": "Communications Director (Japan & Korea)", "location": "Tokyo, Japan", "locations": ["Tokyo, Japan"], "hot": 0, "department": "Tinder", "business_unit": "Communications", "t_update": 1713418227, "t_create": 1692763906, "ats_job_id": "8ed5ab13-c202-4eba-a57a-4ea0b353398c", "display_job_id": "R0003154", "type": "ATS", "id_locale": "R0003154-en", "job_description": "Tinder, the most popular dating app in the world, is searching for a dynamic communications leader to join our remarkable team. Tinder has always been driven by youth culture, and today, Tinder is used mainly by Gen Z. This group continues to influence society and challenge constrictive norms in the most profound ways. No other generation has significantly impacted the workplace, retail consumption, technology, politics, and culture in the same way. It is this generation that we believe are driving the most change that we've ever seen in how we date, who we date and what dating is. Read more about how young people use Tinder today in \u201cA Renaissance in Dating, Driven by Authenticity\u201d, the second Future of Dating Report from Tinder driven by our Communications team. There's never been a more exciting time to work at Tinder! We're doing something meaningful\u2014helping people find their people. And talking about people - Authenticity and Inclusion are hardwired here at Tinder. You'll find opportunities to bring your light and create some best work of your life moments. You'll be proud of the experiences you help create for humans all over the world. We are on a course to revolutionize how young adults meet and connect today; if you want to be a part of that keep reading. This is an exciting opportunity to mold, protect, and energetically share the compelling story of Tinder - as a progressive company, an innovative product, and a pop-cultural brand. The ideal candidate is a self-motivated individual with a profound understanding of the fast-changing media landscape in Japan (experience in Korea/ East Asia comms would be a plus) with an interest in swift decision-making, expert issues management, and spur-of-the-moment creativity. As a generalist role, this role encompasses brand reputation driving strategy (consumer-focused comms), product communications (new features, innovation, and trust & safety) and corporate communications (crisis, reputation management and executive positioning). If you're known for your practical (read: level-headed), willing to take action approach, and creative brilliance, then this is the dream job you've been waiting for! The Communications Director will report directly to the VP of Communications, APAC, and operate from our Tokyo office in a hybrid work environment which will require in office partnership 3x a week. This is not a remote role and requires fluency in English and native reading, writing and speaking skills in Japanese including a really deep and insightful grasp of the Japanese press and both traditional and new media landscape. As the Director of Communications, your scope will include: Orchestrating and leading Japan and Korea/ East Asia communications strategies related to product and company narratives, as well as new product features , pr events and updates Collaborating with cross functional partners within Match Group and Tinder to promote trust, safety, and inclusivity narratives. Partner with regional teams to build impactful global campaigns and brand narratives Devising and implementing a streamlined, concise, and creative local executive communications strategy Directly engaging with media and external partners regularly and consistently to deepen relations and build impactful exposure and understanding for the brand Crafting product-specific messaging and collaborating closely with product marketing, and safety teams. Managing the country budget efficiently and smartly to the benefit of the brand goals Being the spokesperson for ongoing storytelling in the markets Supervising agency resources and planning - balancing critical thinking and work at your end and using agencies effectively for implementation Key Qualifications: While we understand that not every candidate will \u201ctick all the boxes\u201d, we believe our perfect match will resonate with our Tinder Values: One Team, One Dream You're a champion of simplicity, keeping communications clear and uncluttered while being resourceful enough to achieve significant results. Your optimism is infectious, and your ego never gets in the way of the big picture - you operate with humility. You understand that communications as a profession is changing and want to be a part of that change. You're ready to explore new ways of breaking the routine. And bring ideas to the table You bring 10+ years of experience in the communications field, either in a corporate or agency environment. Spark Solutions You've worked with partners and executives, helping to crystalize their vision and express their unique points of view. You appreciate a nimble and flexible environment, where being a Director means you're a leader in charge and can advance high-impact work quickly Own it You're a pro at taking global storytelling into the local market and can work with cross-functional teams to bring collaborative projects to life You consider yourself a hands-on leader with a strong network of relationships with journalists across many verticals. You're not afraid and actually like to pick up the phone on an incoming note from a reporter, good or bad. Embrace our Differences You understand this is more of a generalist role oscillating between product, corporate, and trust & safety. While your focus is on the East Asian markets, you are comfortable working across multiple time zones. You show up as an expert and someone to look up and learn from within the Global comms team and the local market teams Never Stop Learning You possess the ability to devise and implement communications campaigns and can develop and expand capable of developing and expanding ambitious strategies You listen and respond to feedback and demonstrate clearly that you can introspect and evolve. $ - $ a year None As part of our team, you'll enjoy: \\-- Working on a product that has an immediate impact on people's lives all around the world \\-- 12 child care days per child, as well as days off for their graduation and admission interviews \\-- Additional paid days off for weddings, anniversaries, pet checkups, and birthdays \\-- Full reimbursement of commuting costs up to (up to 100,000 yen/month) and housing assistance for full-time employees \\-- Relocation assistance for families with preschool age children who need to be closer to school for enrollment \\-- Other perks include gifts for birthdays, weddings, and maternity leave \\-- Professional Development stipend and No Meeting Wednesdays \\-- The opportunity to join six active Employee Resource Groups (ERGs) \u30c1\u30fc\u30e0\u306e\u4e00\u54e1\u3068\u3057\u3066\u3001\u4ee5\u4e0b\u306e\u7279\u5178\u3084\u798f\u5229\u539a\u751f\u304c\u5f97\u3089\u308c\u307e\u3059\u3002 \\-- \u4e16\u754c\u4e2d\u306e\u4eba\u3005\u306e\u751f\u6d3b\u306b\u30c0\u30a4\u30ec\u30af\u30c8\u306b\u5f71\u97ff\u3092\u4e0e\u3048\u308b\u30d7\u30ed\u30c0\u30af\u30c8\u306b\u643a\u308f\u308b\u3053\u3068\u304c\u3067\u304d\u308b \\-- \u304a\u5b50\u3055\u30931\u4eba\u306b\u3064\u304d\u30011\u5e74\u306712\u65e5\u9593\u306e\u770b\u8b77\u4f11\u6687\u3092\u53d6\u5f97\u53ef\u80fd \\-- \u5165\u5712\u30fb\u5165\u5b66\u30fb\u5352\u5712\u30fb\u5352\u696d\u6642\u306b\u4f11\u6687\u3092\u53d6\u5f97\u53ef\u80fd \\-- \u7d50\u5a5a\u30fb\u7d50\u5a5a\u8a18\u5ff5\u65e5\u30fb\u30da\u30c3\u30c8\u306e\u8a3a\u7642\u65e5\u30fb\u8a95\u751f\u65e5\u3068\u3044\u3063\u305f\u6a5f\u4f1a\u306b\u4f11\u6687\u3092\u53d6\u5f97\u53ef\u80fd \\-- \u4ea4\u901a\u8cbb\u3092\u5168\u984d\u652f\u7d66\uff08\u4e0a\u9650\uff1a100,000\u5186/\u6708\uff09 \\-- \u5bb6\u8cc3\u88dc\u52a9\u5236\u5ea6\u3042\u308a \\-- \u5f85\u6a5f\u5150\u7ae5\u5bfe\u7b56\u306e\u305f\u3081\u3001\u4fdd\u80b2\u5712\u306b\u5165\u308a\u3084\u3059\u3044\u5730\u57df\u306b\u5f15\u3063\u8d8a\u3057\u3092\u3059\u308b\u5834\u5408\u306b\u3001\u5f15\u3063\u8d8a\u3057\u91d1\u63f4\u52a9\u3042\u308a \\-- \u8a95\u751f\u65e5\u30fb\u7d50\u5a5a\u30fb\u51fa\u7523\u306e\u30d7\u30ec\u30bc\u30f3\u30c8 \\-- \u81ea\u5df1\u7814\u945a\u8cbb\u7528\u652f\u7d66\uff081\u4e07\u5186/\u6708\uff09 \\-- \u6c34\u66dc\u65e5\u306b\u306f\u4e00\u5207\u30df\u30fc\u30c6\u30a3\u30f3\u30b0\u306a\u3057 \\-- 6\u3064\u306eERG (\u30de\u30a4\u30ce\u30ea\u30c6\u30a3\u5f93\u696d\u54e1\u3092\u5c0a\u91cd\u3059\u308b\u30b0\u30eb\u30fc\u30d7)\u306b\u5c5e\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u308b \\--Black at Tinder (BET) \\--Desis at Tinder (DAT) \\--East Asians at Tinder (EAT) \\--Latinx at Tinder (LAT) \\--Pride at Tinder (PAT) \\--Women at Tinder (WAT)", "stars": 0.0, "medallionProgram": null, "location_flexibility": null, "work_location_option": "onsite", "canonicalPositionUrl": "https://join.matchgroupcareers.com/careers/job/17634306", "isPrivate": false}, {"id": 20874146, "name": "Global Escalations Specialist", "location": "United States", "locations": ["United States"], "hot": 0, "department": "Tinder", "business_unit": "Community", "t_update": 1715024788, "t_create": 1708628162, "ats_job_id": "efff924e-52bf-4a6f-bd0e-16759be2584d", "display_job_id": "efff924e-52bf-4a6f-bd0e-16759be2584d", "type": "ATS", "id_locale": "644898594071535484-en", "job_description": "Investigate, troubleshoot, and drive our most sensitive complex escalations to resolution, spanning topics across Trust & Safety and member support Investigate bug and app functionality reports and work with Engineering to help them resolve issues Contribute to or own organizational projects and consistently drive process improvements to support the team's goals and objectives. Minimum of 2+ years in a similar role Basic knowledge of Zendesk Ability to understand and summarize complex cases quickly Outstanding analytical skills and excellent judgment Highly flexible and adaptable Ability to multitask, keep up with large volumes of emails in a fast-paced environment Outstanding time management and prioritization skills Quick learner Great teammate Tech savvy Strong attention to details Passion for helping people Ability to use critical thinking and logic to isolate and resolve issues Bachelor degree or higher or equivalent", "stars": 0.0, "medallionProgram": null, "location_flexibility": null, "work_location_option": "onsite", "canonicalPositionUrl": "https://join.matchgroupcareers.com/careers/job/20874146", "isPrivate": false}, {"id": 18109154, "name": "Protect Overwatch - GSOC Manager", "location": "West Hollywood, California", "locations": ["West Hollywood, California"], "hot": 0, "department": "Match Group", "business_unit": "Real Estate & Facilities", "t_update": 1714597640, "t_create": 1695319779, "ats_job_id": "7181cc68-a2a8-4f84-8b7a-5dfe61895c17", "display_job_id": "7181cc68-a2a8-4f84-8b7a-5dfe61895c17", "type": "ATS", "id_locale": "360083768347771209-en", "job_description": "As part of Match Group Protect's Global Security Operations Center (GSOC), you'll help to proactively protect our people, property, and executives. In this role you will help take Protect's GSOC to the next level, including the management of the program, and running all facets, which includes global security operations, CCTV and access control monitoring, global threat assessment, and response to global incidents that require Protect's involvement. Our GSOC is the main location for the monitoring and analysis of risks on a global scale that are relevant to our employees and brands. Know where you belong. Match Group is a leading provider of dating products across the globe. Our portfolio includes Tinder, Match, Hinge, PlentyOfFish, The League, and others, each designed to spark meaningful connections for singles worldwide. Creating a sense of belonging doesn't stop at our products - it's the foundation of every team we hire. This position will require being onsite 5 days/week in our West Hollywood office. How you'll make an impact: Manage the day-to-day operations of our physical security technology, including access control and CCTV with the goal of keeping our employees and buildings safe. Lead the response on a global scale to company-wide response to significant events. You will monitor all-source information and global crises to proactively identify and assess threats that could impact our business. Handle response to notifications, alarms, calls, and emails related to Match Group global operations. Create procedure documentation for operator role guidelines, GSOC structure and SLAs, and incident response. You will help manage global access control and monitor the state of our Protect security technology deployed across our worldwide offices. Develop proactive alerting policies surrounding various security systems, partner with regional offices to deploy alerting updates, and respond to high priority alerts. Support physical threat monitoring, response, and investigation. We could be a match if: You have 3+ years of experience in a GSOC Lead or Management position. You have additional experience in corporate security, intelligence analysis, public sector, protective intelligence, or security systems. You have experience building out GSOCs to support global locations. You have demonstrated knowledge of physical security best practices, physical security systems, investigative techniques, and incident management. You have a demonstrated ability to identify, analyze, and evaluate large quantities of information and to communicate accurate and timely recommendations on business and security related risks. You have experience with mass notification systems and/or incident management applications and experience managing real-life crisis and incident response. You are flexible to work non-traditional schedules including holidays, nights, and weekends to adapt to our 24/7 operation. Certified Fraud Examiner (CFE), Certified Protection Professional (CPP), Certified Forensic Interviewer (CFI), Physical Security Professional (PSP), Professional Certified Investigator (PCI), a plus. $ 94500- $115000 a year Factors such as scope and responsibilities of the position, candidate's work experience, education/training, job-related skills, internal peer equity, as well as market and business considerations may influence base pay offered. This salary range is reflective of a position based in Los Angeles, CA. This salary will be subject to a geographic adjustment (according to a specific city and state), if an authorization is granted to work outside of the location listed in this posting. LI- LH1 Why Match Group? Our mission is simple - to help people find love and happiness! We love our employees too and understand the importance of all life's milestones. Here are some of the benefits we are proud to offer: Mind & Body - Medical, mental health, and wellness benefits to support your overall health and well-being Financial Wellness - Competitive compensation, 100% employer match on 401k contributions up to 10% (cap at $10,000), as well as an employee stock purchase program to help you feel supported in your financial security Unplug - Generous PTO and 18 paid holidays so you can unplug Career - Annual training allowance for professional development and ERG membership opportunities and events so you feel connected and empowered in your work Family - Families come in all shapes and sizes so we offer 20 weeks of 100% paid parental leave, fertility, adoption, and child care resources, as well as pet insurance and discounts Company Gatherings - We host fun happy hours and company events where our employees get to know each other and build a sense of connection and belonging! We are proud to be an equal opportunity employer and we value the rich dynamics that diversity brings to our company. We do not discriminate on the basis of race, religion, color, creed, national origin, ancestry, disability, marital status, age, sexual orientation, sex (including pregnancy and sexual harassment), gender identity or expression, uniformed service or veteran status, genetic information, or any other legally protected characteristic. Period. If you require reasonable accommodation to complete a job application, pre-employment testing, or a job interview or to otherwise participate in the hiring process, please contact employeebenefits@matchgroup.com.", "stars": 0.0, "medallionProgram": null, "location_flexibility": null, "work_location_option": "onsite", "canonicalPositionUrl": "https://join.matchgroupcareers.com/careers/job/18109154", "isPrivate": false}, {"id": 22207678, "name": "Sr. Machine Learning Software Engineer", "location": "Seoul", "locations": ["Seoul"], "hot": 0, "department": "Hyperconnect", "business_unit": "AI/ML \uc9c1\uad70 \uc9d1\uc911\ucc44\uc6a9", "t_update": 1714641047, "t_create": 1714640998, "ats_job_id": "5e4bf21b-a890-4f3d-a033-c4c845470500", "display_job_id": "5e4bf21b-a890-4f3d-a033-c4c845470500", "type": "ATS", "id_locale": "712055874626384982-en", "job_description": "\ud558\uc774\ud37c\ucee4\ub125\ud2b8 Machine Learning Software Engineer (MLSE)\ub294 \uc0ac\ub78c\uacfc \uc0ac\ub78c \uc0ac\uc774\ub97c \uc5f0\uacb0\ud558\ub294 \uc11c\ube44\uc2a4\uc5d0\uc11c, \uc18c\ud504\ud2b8\uc6e8\uc5b4 \uc5d4\uc9c0\ub2c8\uc5b4\ub9c1\uc744 \ud1b5\ud574 \uba38\uc2e0\ub7ec\ub2dd \uae30\uc220\uc744 \uc11c\ube44\uc2a4\uc5d0 \uc801\uc6a9\ud558\uace0 \uc0ac\uc6a9\uc790 \uacbd\ud5d8\uc744 \ud601\uc2e0\ud558\ub294 \uc77c\uc744 \ud569\ub2c8\ub2e4. \uc0ac\ub0b4 AI \uc870\uc9c1\uc5d0\uc11c \ub9cc\ub4e0 \uc601\uc0c1/\uc74c\uc131/\ubb38\uc790/\ucd94\ucc9c \ub4f1\uc744 \ud3ec\ud568\ud558\uc5ec \ub2e4\uc591\ud55c \ub3c4\uba54\uc778\uc758 \uc218\ub9ce\uc740 \ubaa8\ub378\uc744 \ud504\ub85c\ub355\uc158\uc5d0 \uc801\uc6a9\ud558\uace0, \ubaa8\ubc14\uc77c \ubc0f \ud074\ub77c\uc6b0\ub4dc \uc11c\ubc84\ub97c \ud1b5\ud574 \uc548\uc815\uc801\uc73c\ub85c \uc81c\uacf5\ud558\uba74\uc11c \ub9c8\uc8fc\ud558\ub294 \uc5d4\uc9c0\ub2c8\uc5b4\ub9c1 \ubb38\uc81c\ub4e4\uc744 \ud574\uacb0\ud558\uba70, \uc6b0\ub9ac\uac00 \ub9cc\ub4e4\uc5b4 \ub0b4\ub294 \uae30\uc220\uc774 \uc2e4\uc81c \uc11c\ube44\uc2a4\uc758 \uc131\uc7a5\uc5d0 \uae30\uc5ec\ud558\ub294 \uac83\uc744 \ubaa9\ud45c\ub85c \ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubaa9\ud45c \uc544\ub798 \ud558\uc774\ud37c\ucee4\ub125\ud2b8\uc758 ML Software Engineer\ub294 \uc544\uc790\ub974, \ud558\ucfe0\ub098 \ub4f1 \ud558\uc774\ud37c\ucee4\ub125\ud2b8\uc758 \uc5ec\ub7ec \uc81c\ud488\ub4e4\uc5d0 \uae30\uc5ec\ud558\ub294 \uba38\uc2e0\ub7ec\ub2dd \uae30\uc220\ub4e4\uc744 \uc218\ub144\uac04 \ubc1c\uc804\uc2dc\ucf1c \ub098\uac00\uace0 \uc788\uc73c\uba70, \uc774\ub807\uac8c \ucd95\uc801\ub41c \uae30\uc220\ub4e4\uc744 S&P 500 \ud3b8\uc785\uc0ac\uc778 \ub9e4\uce58\uadf8\ub8f9\uc758 \ub2e4\uc591\ud55c \uae00\ub85c\ubc8c \ube44\uc988\ub2c8\uc2a4 \uc11c\ube44\uc2a4\uc5d0\ub3c4 \uc190\uc27d\uac8c \ud65c\uc6a9\ud558\uae30 \uc704\ud55c \uae30\uc220\uc744 \uac1c\ubc1c\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. ML Software Engineer\ub294 \uc6b0\ub9ac\uac00 \ubcf4\uc720\ud55c \ubaa8\ub4e0 AI \uae30\uc220\uc744 \uc81c\ud488\uc5d0 \uc801\uc6a9\ud558\uc5ec \ube44\uc988\ub2c8\uc2a4 \uc784\ud329\ud2b8\ub97c \ub0bc \uc218 \uc788\ub3c4\ub85d \ud558\uba70, \uc9c0\uc18d \uac00\ub2a5\ud55c \uc2dc\uc2a4\ud15c\uc744 \uac1c\ubc1c\ud558\uc5ec AI \uae30\uc220\uc758 \uc801\uc6a9\uc774 \uac00\uc18d\ud654 \ub418\ub3c4\ub85d \ud558\ub294 \uac83\uc744 \ubaa9\ud45c\ub85c \ud569\ub2c8\ub2e4. \uc774 \ubaa9\ud45c\ub97c \ub2ec\uc131\ud558\uae30 \uc704\ud574 (1) ML \ubaa8\ub378 \uae30\ubc18\uc758 \ud655\uc7a5\uc131 \uc788\ub294 \ubc31\uc5d4\ub4dc \uc11c\ubc84\ub97c \uac1c\ubc1c\ud558\uba70, (2) ML \ubaa8\ub378 \ucd94\ub860\uc744 \uc704\ud55c \uc2e4\uc2dc\uac04 \ub370\uc774\ud130 \ud30c\uc774\ud504\ub77c\uc778\uc744 \uac1c\ubc1c/\uc6b4\uc601\ud558\uace0, (3) \ub2e4\ub978 \ud300\ub4e4\uacfc \ud611\uc5c5\ud558\uc5ec \uacf5\ud1b5\uc73c\ub85c \uad00\ub9ac\ud560 \uc218 \uc788\ub294 ML \ucef4\ud3ec\ub10c\ud2b8\ub97c \ud50c\ub7ab\ud3fc\ud654\ud558\ub294 \uc791\uc5c5\uae4c\uc9c0 \uc218\ud589\ud569\ub2c8\ub2e4. \uadf8 \uacfc\uc815\uc5d0\uc11c \ub2e4\ub978 \uc9c1\uad70\uacfc\ub3c4 \ubc00\uc811\ud558\uac8c \ud611\uc5c5\ud558\uba74\uc11c KPI \ub2ec\uc131\uc5d0 \ub3c4\ub2ec\ud558\uae30 \uc704\ud574 \ud544\uc694\ub85c \ud558\ub294 \ubaa8\ub4e0 \uacfc\uc815(\ubb38\uc81c \uc815\uc758, \uac00\uc124 \uc124\uc815, \uc2e4\ud5d8 \uc124\uacc4, \ubd84\uc11d \ubc0f \ud53c\ub4dc\ubc31)\uc5d0 \ucc38\uc5ec\ud558\uba70 \uc8fc\ub3c4\uc801\uc73c\ub85c \uc5c5\ubb34\ub97c \uc9c4\ud589\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc77c\ud558\ub294 \ubaa8\uc2b5\uc5d0 \ub300\ud55c \uc870\uae08 \ub354 \uc790\uc138\ud55c \uc774\uc57c\uae30\ub294 \ub2e4\uc74c\uc758 \ub0b4\uc6a9\uc744 \ucc38\uace0\ud558\uc2dc\uba74 \uc88b\uc2b5\ub2c8\ub2e4. AI Platform Dev Team \uc778\ud130\ubdf0 ML Application Team \uc778\ud130\ubdf0 Head of AI - Shurain \uc778\ud130\ubdf0 AI in Social Discovery (Blending Research and Production) ML Software Engineer\ub4e4\uc774 \ud480\uace0 \uc788\ub294 \uc7ac\ubbf8\uc788\ub294 \ubb38\uc81c\ub4e4\uc740 Tech blog\uc5d0 \uc5c5\ub85c\ub4dc \ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. ksqlDB\ub97c \uc774\uc6a9\ud558\uc5ec \uc2e4\uc2dc\uac04 ML \ud53c\uccd0 \ub370\uc774\ud130\ub97c \uacc4\uc0b0\ud558\uae30 \uace0\uc131\ub2a5 ML \ubc31\uc5d4\ub4dc\ub97c \uc704\ud55c 10\uac00\uc9c0 Python \uc131\ub2a5 \ucd5c\uc801\ud654 \ud301 \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378 \uc11c\ube59 \ube44\uc6a9 1/4\ub85c \uc904\uc774\uae30 \uc774\ubca4\ud2b8 \uae30\ubc18\uc758 \ub77c\uc774\ube0c \uc2a4\ud2b8\ub9ac\ubc0d \ucd94\ucc9c \uc2dc\uc2a4\ud15c \uc6b4\uc6a9\ud558\uae30 \uba38\uc2e0\ub7ec\ub2dd \uc5b4\ud50c\ub9ac\ucf00\uc774\uc158\uc744 \uc704\ud55c \ub370\uc774\ud130 \uc800\uc7a5\uc18c \uae30\uc220 \uc5c5\ubb34 \ud658\uacbd [\uad6d\ub0b4 \ucd5c\uc0c1\uc704 \uc218\uc900\uc758 AI \uc870\uc9c1] \ud0d1\ud2f0\uc5b4 AI/ML \ud559\ud68c\uc5d0 \ub17c\ubb38\uc744 \uc8fc\uae30\uc801\uc73c\ub85c \ucd9c\ud310\ud558\ub294 Machine Learning Engineer \ubc0f Machine Learning Research Scientist \ub4e4\uacfc \ud568\uaed8 \uc77c\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud558\uc774\ud37c\ucee4\ub125\ud2b8\uc5d0\uc11c \ucd9c\ud310\ud55c \ub17c\ubb38\uc740 \uc5ec\uae30\uc11c \ud655\uc778\ud574\ubcf4\uc2e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4. [\ud48d\ubd80\ud55c MLOps \ub178\ud558\uc6b0] 50\uac1c \uc774\uc0c1\uc758 \ubaa8\ub378\uc744 \ud504\ub85c\ub355\uc158\uc5d0\uc11c \uc0ac\uc6a9\ud558\uace0 \uc788\uc744 \ub9cc\ud07c, MLOps\uc5d0 \ub300\ud55c \ub178\ud558\uc6b0\uac00 \ud48d\ubd80\ud55c \uc870\uc9c1\uc5d0\uc11c \uc77c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. Deview 2023 - \uc2e4\uc2dc\uac04 \ucd94\ucc9c \uc2dc\uc2a4\ud15c\uc744 \uc704\ud55c Feature Store \uad6c\ud604\uae30 Deview 2021 - \uc5b4\ub5bb\uac8c \ub354 \ub9ce\uc740 \ubaa8\ub378\uc744 \ub354 \ube60\ub974\uac8c \ubc30\ud3ec\ud560 \uac83\uc778\uac00? \ucd08\uace0\uc131\ub2a5 \ub525\ub7ec\ub2dd \ud074\ub7ec\uc2a4\ud130 \uad6c\ucd95\ud558\uae30 1\ud3b8 \ub2f4\ub2f9 \uc5c5\ubb34 \ud558\uc774\ud37c\ucee4\ub125\ud2b8\ub294 \uc81c\ud488\uc5d0 \uba38\uc2e0\ub7ec\ub2dd \uae30\uc220\uc744 \uc801\uc6a9\ud558\uae30 \uc704\ud574 \ub2e4\uc591\ud55c \ubc29\uba74\uc73c\ub85c \ub178\ub825\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud558\uc774\ud37c\ucee4\ub125\ud2b8\uc758 ML Software Engineer\ub294 \ud06c\uac8c \ub2e4\uc74c\uacfc \uac19\uc740 4\uac00\uc9c0 \ubd84\uc57c \uc911 \ud55c \uac00\uc9c0 \uc774\uc0c1\uc758 \uc5c5\ubb34\ub97c \uc218\ud589\ud558\uac8c \ub429\ub2c8\ub2e4. [\uba38\uc2e0\ub7ec\ub2dd \uae30\ubc18\uc758 \ubc31\uc5d4\ub4dc \uc5b4\ud50c\ub9ac\ucf00\uc774\uc158 \uc124\uacc4 \ubc0f \uad6c\ud604] \ud558\uc774\ud37c\ucee4\ub125\ud2b8\uc640 \ub9e4\uce58\uadf8\ub8f9\uc5d0\uc11c \uc6b4\uc601 \uc911\uc778 \uc11c\ube44\uc2a4\ub4e4\uc758 \ud488\uc9c8\uc744 \ub192\uc774\uae30 \uc704\ud55c \ub2e4\uc591\ud55c \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc18\uc758 \ubc31\uc5d4\ub4dc \uc11c\ube44\uc2a4 (FastAPI, Spring)\ub97c \uac1c\ubc1c\ud569\ub2c8\ub2e4. \uae00\ub85c\ubc8c \uc2a4\ucf00\uc77c\uc5d0\uc11c \uc2e4\uc2dc\uac04 \ub3d9\uc791\uc774 \uac00\ub2a5\ud558\ub3c4\ub85d \uc131\ub2a5 \uad00\uc810\uc5d0\uc11c\uc758 \ub9ce\uc740 \uace0\ubbfc\uacfc \ud568\uaed8 \uc124\uacc4\ub418\uace0 \uc788\uc73c\uba70, \ud300\uc5d0\uc11c \uc6b4\uc601 \uc911\uc778 \ub9c8\uc774\ud06c\ub85c\uc11c\ube44\uc2a4\ub4e4\uc740 \uc804\uc0ac \ub0b4\uc5d0\uc11c\ub3c4 \uac00\uc7a5 \ub192\uc740 \uc218\uc900\uc758 \ud2b8\ub798\ud53d\uc744 \ucc98\ub9ac\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uac1c\uc778\ud654 \ucd94\ucc9c \uc2dc\uc2a4\ud15c \ubc0f \uac80\uc0c9 \uc2dc\uc2a4\ud15c (Azar, Hakuna, \ub9e4\uce58\uadf8\ub8f9 \ub0b4\uc758 \uc11c\ube44\uc2a4\ub4e4) \uc9c0\uc18d\uc801\uc73c\ub85c \ubaa8\ub378\uc744 \uac1c\uc120\ud558\ub294 AI Flywheel \uc2dc\uc2a4\ud15c \uac1c\ubc1c Trust & Safety\ub97c \uc704\ud55c \ubaa8\ub354\ub808\uc774\uc158 \uc2dc\uc2a4\ud15c \ubc0f \ud50c\ub7ab\ud3fc \uac1c\ubc1c \uc601\uc0c1, \uc74c\uc131 \ubc0f \uc790\uc5f0\uc5b4 \ub4f1\uc758 \ube44\uc815\ud615 \ub370\uc774\ud130\ub97c \ud574\uc11d \uac00\ub2a5\ud55c \ud615\ud0dc\ub85c \ubcc0\ud658\ud574 \uc8fc\ub294 \uc0ac\ub0b4 API \uc11c\ubc84 [\ubaa8\ub378 \ucd94\ub860\uc744 \uc704\ud55c \uc2e4\uc2dc\uac04 \ub370\uc774\ud130 \ud30c\uc774\ud504\ub77c\uc778 \uac1c\ubc1c] \uc2e4\uc2dc\uac04 \uc774\ubca4\ud2b8\ub97c \uac00\uacf5\ud558\uc5ec \ubaa8\ub378 \ucd94\ub860\uc5d0 \ud65c\uc6a9\ud560 \uc218 \uc788\uac8c \ud558\ub294 \ud30c\uc774\ud504\ub77c\uc778(Apache Flink, KSQL)\uc744 \uac1c\ubc1c\ud569\ub2c8\ub2e4. \ud53c\uccd0\ub97c \ube60\ub974\uace0 \uc548\uc815\uc801\uc73c\ub85c \uc218\uc9d1, \uac00\uacf5, \uc11c\ube59\ud558\uae30 \uc704\ud55c \uc2dc\uc2a4\ud15c(ex. streaming application, feature store)\uc744 \uace0\ubbfc\ud558\uace0 \uc124\uacc4\ud569\ub2c8\ub2e4. \ub54c\ub85c\ub294 \ud30c\uc774\ud504\ub77c\uc778 \uac1c\ubc1c \uacfc\uc815\uc5d0\uc11c \ubaa8\ub378 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0a4\ub294 \ud53c\uccd0\ub97c \uc120\uc81c\uc801\uc73c\ub85c \ubc1c\uad74\ud558\uae30\ub3c4 \ud569\ub2c8\ub2e4. [\uba38\uc2e0\ub7ec\ub2dd \uae30\ub2a5\uc774 \ud0d1\uc7ac\ub41c \ud074\ub77c\uc774\uc5b8\ud2b8 SDK \uac1c\ubc1c] \ud558\uc774\ud37c\ucee4\ub125\ud2b8\uc640 \ub9e4\uce58\uadf8\ub8f9\uc758 \ubaa8\ubc14\uc77c \uc5b4\ud50c\ub9ac\ucf00\uc774\uc158\uc5d0 \ud0d1\uc7ac\ub418\ub294 \uba38\uc2e0\ub7ec\ub2dd SDK\ub97c \uac1c\ubc1c\ud558\uace0 \uc6b4\uc601\ud569\ub2c8\ub2e4. \ud558\uc774\ud37c\ucee4\ub125\ud2b8\uc5d0\uc11c \uac1c\ubc1c\ud558\ub294 SDK\ub294 \uc804 \uc138\uacc4 \uac01\uc9c0\uc5d0\uc11c \uc0ac\uc6a9\ub418\ub294 \uc218\ub9ce\uc740 \ub514\ubc14\uc774\uc2a4\uc5d0\uc11c \ub3d9\uc791\ud560 \uc218 \uc788\uc5b4\uc57c \ud558\uae30\uc5d0, \ub9e4\uc6b0 \ub192\uc740 \uc548\uc815\uc131\uc744 \ubaa9\ud45c\ub85c \ud558\uba70 \uac1c\ubc1c\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ub354\ubd88\uc5b4 \ucd5c\uace0 \uc131\ub2a5\uc758 \uba38\uc2e0\ub7ec\ub2dd API\ub97c \uc81c\uacf5\ud558\uae30 \uc704\ud574 \uc9c0\uc18d\uc801\uc73c\ub85c \uc5c5\ub370\uc774\ud2b8\ub420 \uc218 \uc788\uc5b4\uc57c \ud558\uba70, \uc774\ub97c \uc704\ud574 Scalable \ud55c \ubc31\uc5d4\ub4dc \ud50c\ub7ab\ud3fc\ub3c4 \ud568\uaed8 \uac1c\ubc1c\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. [\uba38\uc2e0\ub7ec\ub2dd \uae30\ubc18\uc758 \uc2e0\uaddc \uc11c\ube44\uc2a4 PoC] \ud558\uc774\ud37c\ucee4\ub125\ud2b8\ub294 \uc81c\ud488\uc5d0 \uae30\uc5ec\ud558\ub294 \ub2e4\uc591\ud55c \uba38\uc2e0\ub7ec\ub2dd \uae30\uc220\uc744 \uc5f0\uad6c \ubc0f \uac1c\ubc1c\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc0ac\ub0b4\uc5d0\uc11c \uac1c\ubc1c\ub41c \uc0c8\ub85c\uc6b4 \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc758 \ube44\uc988\ub2c8\uc2a4 \uc784\ud329\ud2b8\ub97c \uce21\uc815\ud558\uae30 \uc704\ud574\uc11c\ub294 Proof of Concept(PoC)\uac00 \ud544\uc694\ud55c \uacbd\uc6b0\uac00 \ub9ce\uc740\ub370, MLSE\ub294 PoC \ud504\ub85c\ub355\ud2b8\ub97c \uac1c\ubc1c\ud558\ub294 \uc5ed\ud560\uc744 \ud558\uae30\ub3c4 \ud569\ub2c8\ub2e4. PoC \ud504\ub85c\ub355\ud2b8 \uac1c\ubc1c\uc744 \ud1b5\ud574 \uac1c\ubc1c \uc0ac\uc774\ud074\uc744 \uc9e7\uac8c \uc904\uc774\uace0 \ub354 \ub9ce\uc740 \uc774\ud130\ub808\uc774\uc158\uc744 \ub3cc\uac8c \ud568\uc73c\ub85c\uc368, \ubaa8\ub378 \uac1c\uc120\uc774 \ub354 \ube60\ub974\uac8c \uc774\ub904\uc9c0\ub3c4\ub85d \ud569\ub2c8\ub2e4. Sr. Machine Learning Software Engineer\ub294, \uc704 4\uac00\uc9c0 \uc5c5\ubb34 \uc774\uc678\uc5d0\ub3c4 \ub2e4\uc591\ud55c \uae30\uc220 \ud504\ub85c\uc81d\ud2b8\ub97c \ub9ac\ub529\ud558\uace0 \ube44\uc988\ub2c8\uc2a4 \uc9c0\ud45c\ub97c \uc0c1\uc2b9\uc2dc\ud0a4\uae30 \uc704\ud55c \uc791\uc5c5\ub4e4\uc744 \uc218\ud589\ud569\ub2c8\ub2e4. Sr. MLSE\ub294 \ud300\uc758 \uc2dc\ub2c8\uc5b4\ub85c\uc11c \ud004\ub9ac\ud2f0 \ub192\uc740 \ucf54\ub4dc \ub9ac\ubdf0, \ud398\uc5b4 \ud504\ub85c\uadf8\ub798\ubc0d, \ud655\uc7a5\uc131 \uc788\ub294 \uc2dc\uc2a4\ud15c \uc124\uacc4, \ud300\uc5d0 \ubaa8\ubc94\uc774 \ub418\ub294 \uae30\uc220 \ubb38\uc11c \uc791\uc131, \uc548\uc815\uc131\uc744 \uc704\ud55c \ubaa8\ub2c8\ud130\ub9c1 \uc2dc\uc2a4\ud15c \uad6c\ucd95\ud558\ub294 \ub4f1\uc758 \uc791\uc5c5\uc5d0 \ucc45\uc784\uc774 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ubc16\uc5d0\ub3c4, \ud300\uc758 \uc18c\ud504\ud2b8\uc6e8\uc5b4 \uc5d4\uc9c0\ub2c8\uc5b4\ub9c1 \uc5ed\ub7c9\uc744 \ud55c\uce35 \ub04c\uc5b4\uc62c\ub9ac\uae30 \uc704\ud55c \ub2e4\uc591\ud55c \uc791\uc5c5\uc744 \uc81c\uc548\ud558\uace0 \uc218\ud589\ud569\ub2c8\ub2e4. Sr. MLSE\ub294 \uc5d4\uc9c0\ub2c8\uc5b4\ub9c1 \uc791\uc5c5 \uc774\uc678\uc5d0\ub3c4 \ud68c\uc0ac\uc758 \uc131\uc7a5\uc744 \uc704\ud574 \ub2e4\uc591\ud55c \uc5ed\ud560\uc744 \uc218\ud589\ud569\ub2c8\ub2e4. Cross-team \ud504\ub85c\uc81d\ud2b8\uc5d0 \ucc38\uc5ec\ud558\uba74\uc11c \ub2e4\uc591\ud55c \ud300\uc758 \uad6c\uc131\uc6d0\ub4e4\uacfc \ucee4\ubba4\ub2c8\ucf00\uc774\uc158\ud558\uc5ec \ud504\ub85c\uc81d\ud2b8\uc758 \uc131\uacf5\uc744 \uc774\ub04c\uc5b4 \ub098\uac11\ub2c8\ub2e4. \ud300\uc758 \uc8fc\ub2c8\uc5b4 \uc5d4\uc9c0\ub2c8\uc5b4\ub4e4\uc774 \uc131\uc7a5\ud560 \uc218 \uc788\ub3c4\ub85d \ub2e4\uc591\ud55c \ubc29\uba74\uc73c\ub85c \uace0\ubbfc\ud558\uace0 \uae30\uc5ec\ud558\ub294 \uac83 \ub610\ud55c Sr. MLSE\uc758 \uc911\uc694\ud55c \uc5ed\ud560 \uc911 \ud558\ub098\uc785\ub2c8\ub2e4. \uc9c0\uc6d0 \uc790\uaca9 \uae30\uc220 \ud504\ub85c\uc81d\ud2b8\ub97c \ub9ac\ub529\ud558\uace0, \ub2e4\ub978 \uc5d4\uc9c0\ub2c8\uc5b4\uc758 \uc131\uc7a5\uc744 \ub3c4\uc640\ubcf8 \uacbd\ud5d8\uc774 \uc788\uc73c\uc2e0 \ubd84 \ub2e8\uae30\uc801/\uc7a5\uae30\uc801 \uad00\uc810\uc744 \ubaa8\ub450 \uace0\ub824\ud558\uc5ec \uc88b\uc740 \uae30\uc220 \uad00\ub828 \uc758\uc0ac\uacb0\uc815\uc744 \ud558\uc2e4 \uc218 \uc788\ub294 \ubd84 CS fundamentals (\uc6b4\uc601\uccb4\uc81c, \ucef4\ud4e8\ud130 \uc2dc\uc2a4\ud15c \uc544\ud0a4\ud14d\ucc98, \uc790\ub8cc\uad6c\uc870 \ubc0f \uc54c\uace0\ub9ac\uc998)\uc5d0 \ub300\ud55c \ud0c4\ud0c4\ud55c \uae30\ubcf8\uc9c0\uc2dd\uc744 \ubcf4\uc720\ud558\uc2e0 \ubd84 ML \ubaa8\ub378\uc744 \uc11c\ube44\uc2a4\uc5d0 \ubc30\ud3ec\ud558\uae30 \uc704\ud574, \uc5ec\ub7ec \uc9c1\uad70\uc758 \uc774\ud574\uad00\uacc4\uc790\uc640 \ud611\uc5c5\ud560 \uc218 \uc788\ub294 \uac15\ub825\ud55c \ucee4\ubba4\ub2c8\ucf00\uc774\uc158 \ub2a5\ub825\uacfc \uc5d4\uc9c0\ub2c8\uc5b4\ub9c1 \uc5ed\ub7c9\uc744 \uac16\ucd94\uc2e0 \ubd84 \uacf5\ub3d9\uc758 \ubaa9\ud45c \ub2ec\uc131\uc744 \uc704\ud574 \uc11c\ub85c \ud611\ub825\ud558\uace0 \uc11c\ub85c\uc5d0\uac8c \ub3c4\uc6c0\uc744 \uc694\uccad\ud560 \uc218 \uc788\ub294 \uae0d\uc815\uc801\uc774\uace0 \uc0c1\ud638 \uc874\uc911\ud558\ub294 \ud300\uc6cc\ud06c \uc790\uc138\ub97c \ubcf4\uc720\ud558\uc2e0 \ubd84 Java, Kotlin, Python, JavaScript, Go \uc911 \ud55c\uac00\uc9c0 \uc774\uc0c1 \uc5b8\uc5b4\uc5d0 \ub2a5\uc219\ud558\uace0, \uadf8 \uc678 \ud504\ub85c\uadf8\ub798\ubc0d \uc5b8\uc5b4\ub97c \ud559\uc2b5\ud558\uc5ec \uc0ac\uc6a9\uc774 \uac00\ub2a5\ud558\uc2e0 \ubd84 \ud559\uc704\ub098 \uad6d\uc801\uc740 \ubb34\uad00\ud569\ub2c8\ub2e4. \uc6b0\ub300 \uc0ac\ud56d \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc758 \ud559\uc2b5\ubd80\ud130 \uc11c\ube44\uc2a4 \ubc30\ud3ec\uae4c\uc9c0 \ud574\ubcf4\uc2e0 \uacbd\ud5d8\uc774 \uc788\uc73c\uc2e0 \ubd84 \ucd94\ucc9c, \uc0dd\uc131\ud615 AI \ub4f1 \ucd5c\uc2e0 ML \uae30\uc220\uc744 \ud65c\uc6a9\ud55c \uc5b4\ud50c\ub9ac\ucf00\uc774\uc158 \uac1c\ubc1c \ubc0f \ud504\ub85c\ub355\uc158 \uc6b4\uc601 \uacbd\ud5d8\uc774 \uc788\uc73c\uc2e0 \ubd84 \uc2e4\uc81c \uc11c\ube44\uc2a4\uc5d0 AI \uae30\uc220\uc744 \ud1b5\ud569\ud558\uace0 \uc8fc\uc694 \uc9c0\ud45c\ub97c \uc720\uc758\ubbf8\ud558\uac8c \ud5a5\uc0c1\uc2dc\ucf1c \ubcf8 \uacbd\ud5d8\uc774 \uc788\uc73c\uc2e0 \ubd84 \uc5d4\uc9c0\ub2c8\uc5b4\ub9c1 \ud300\uc744 \ub9ac\ub4dc\ud574\ubcf8 \uacbd\ud5d8\uc774 \uc788\uc73c\uc2e0 \ubd84 \ub300\uaddc\ubaa8 \ud2b8\ub798\ud53d\uc744 \ucc98\ub9ac\ud558\ub294 \ubc31\uc5d4\ub4dc \uc11c\ubc84 \uac1c\ubc1c \uacbd\ud5d8\uc774 \uc788\uc73c\uc2e0 \ubd84 RDBMS \ud639\uc740 DynamoDB, Cassandra \ub4f1 \ubd84\uc0b0 \ub370\uc774\ud130\ubca0\uc774\uc2a4\uc5d0 \ub300\ud55c \uc774\ud574\uac00 \uc788\uc73c\uc2e0 \ubd84 Spark, Flink \ub4f1 \ub300\uc6a9\ub7c9/\uc2e4\uc2dc\uac04 \ucc98\ub9ac\ub97c \uc704\ud55c \ubd84\uc0b0/\ubcd1\ub82c \uc2dc\uc2a4\ud15c \uac1c\ubc1c \uacbd\ud5d8\uc774 \uc788\uc73c\uc2e0 \ubd84 \uc601\uc5b4\uc5d0 \ub2a5\ud1b5\ud558\uc2e0 \ubd84 $ - $ a year None \ud558\uc774\ud37c\ucee4\ub125\ud2b8\uac00 \ucc44\uc6a9\ud558\ub294 \ud3ec\uc9c0\uc158\uc5d0 \uc9c0\uc6d0\ud558\ub294 \uacbd\uc6b0, \uac1c\uc778\uc815\ubcf4 \ucc98\ub9ac\uc5d0 \uad00\ud558\uc5ec\uc11c\ub294 \ubcf8 \uac1c\uc778\uc815\ubcf4\ucc98\ub9ac\ubc29\uce68\uc774 \uc801\uc6a9\ub429\ub2c8\ub2e4: https://career.hyperconnect.com/privacy", "stars": 0.0, "medallionProgram": null, "location_flexibility": null, "work_location_option": "onsite", "canonicalPositionUrl": "https://join.matchgroupcareers.com/careers/job/22207678", "isPrivate": false}, {"id": 22203755, "name": "Sr. Machine Learning Engineer", "location": "Seoul", "locations": ["Seoul"], "hot": 0, "department": "Hyperconnect", "business_unit": "AI/ML \uc9c1\uad70 \uc9d1\uc911\ucc44\uc6a9", "t_update": 1714640200, "t_create": 1714611467, "ats_job_id": "d5ae2422-083a-4d82-9e5c-846bbfb3acb5", "display_job_id": "d5ae2422-083a-4d82-9e5c-846bbfb3acb5", "type": "ATS", "id_locale": "1605921881947347779-en", "job_description": "\ud558\uc774\ud37c\ucee4\ub125\ud2b8 Machine Learning Engineer (MLE) \ub294 \uc0ac\ub78c\uacfc \uc0ac\ub78c \uc0ac\uc774\ub97c \uc5f0\uacb0\ud558\ub294 \uc11c\ube44\uc2a4\uc5d0\uc11c, \uae30\uc874\uc758 \uae30\uc220\ub85c\ub294 \uc811\uadfc\ud558\uae30 \uc5b4\ub835\uc9c0\ub9cc \uba38\uc2e0\ub7ec\ub2dd \uae30\uc220\uc744 \ud1b5\ud574 \ud480 \uc218 \uc788\ub294 \ubb38\uc81c\ub4e4\uc744 \ucc3e\uc544\ub0b4\uace0 \ud574\uacb0\ud558\uc5ec \uc0ac\uc6a9\uc790 \uacbd\ud5d8\uc744 \ud601\uc2e0\ud569\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 \uc601\uc0c1/\uc74c\uc131/\ubb38\uc790 \ub4f1\uc744 \ud3ec\ud568\ud558\uc5ec \ub2e4\uc591\ud55c \ub3c4\uba54\uc778\uc758 \uc218\ub9ce\uc740 \ubaa8\ub378\uc744 \uac1c\ubc1c\ud558\uace0, \ubaa8\ubc14\uc77c \ubc0f \ud074\ub77c\uc6b0\ub4dc \uc11c\ubc84\ub97c \ud1b5\ud574 \uc548\uc815\uc801\uc73c\ub85c \uc81c\uacf5\ud558\uba74\uc11c \ub9c8\uc8fc\ud558\ub294 \uc5f0\uad6c \uc8fc\uc81c\ub4e4\uc744 \ud480\uc5b4\ub0b4\uc5b4 \uc6b0\ub9ac\uac00 \ub9cc\ub4e4\uc5b4 \ub0b4\ub294 \uae30\uc220\uc774 \uc2e4\uc81c \uc11c\ube44\uc2a4\uc758 \uc131\uc7a5\uc5d0 \uae30\uc5ec\ud558\ub294 \uac83\uc744 \ubaa9\ud45c\ub85c \ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubaa9\ud45c \uc544\ub798 \ud558\uc774\ud37c\ucee4\ub125\ud2b8\uc758 ML Engineer\ub294 \uc544\uc790\ub974, \ud558\ucfe0\ub098 \ub4f1 \ud558\uc774\ud37c\ucee4\ub125\ud2b8\uc758 \uc5ec\ub7ec \uc81c\ud488\ub4e4\uc5d0 \uae30\uc5ec\ud558\ub294 \uba38\uc2e0\ub7ec\ub2dd \uae30\uc220\ub4e4\uc744 \uc218\ub144\uac04 \ubc1c\uc804\uc2dc\ucf1c \ub098\uac00\uace0 \uc788\uc73c\uba70, \uc774\ub807\uac8c \ucd95\uc801\ub41c \uae30\uc220\ub4e4\uc744 \ub2e4\uc591\ud55c \uae00\ub85c\ubc8c \ube44\uc988\ub2c8\uc2a4 \uc11c\ube44\uc2a4\uc5d0\ub3c4 \uc190\uc27d\uac8c \ud65c\uc6a9\ud558\uae30 \uc704\ud55c \ubc29\uc548\uc744 \uc5f0\uad6c\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. ML Engineer\ub294 \ucd5c\ucca8\ub2e8\uc758 \ubaa8\ub378\uc744 \uc5f0\uad6c\ud558\uace0 \uac1c\uc120\ud558\ub294 \uacfc\ud559\uc790\ub85c\uc11c\uc758 \uc5f0\uad6c \ub2a5\ub825\uacfc, \ub9cc\ub4e4\uc5b4\uc9c4 \ubaa8\ub378\uc758 \uc2dc\uac04/\uacf5\uac04\uc801 \ubcf5\uc7a1\ub3c4\ub97c \uace0\ub824\ud574 \ucd94\ub860 \uc131\ub2a5\uc744 \uadf9\ud55c\uc73c\ub85c \ub04c\uc5b4\uc62c\ub9ac\ub294 \uacf5\ud559\uc790\ub85c\uc11c\uc758 \uac1c\ubc1c \ub2a5\ub825\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc774\ub7f0 \ub2a5\ub825\uc744 \ubc14\ud0d5\uc73c\ub85c \uc2e4\uc81c \uc11c\ube44\uc2a4\uc5d0\uc11c \uacaa\ub294 \ubb38\uc81c\ub97c \ubc1c\uacac/\uc815\uc758\ud558\uace0, \ubb38\uc81c\ud574\uacb0\uc744 \uc704\ud55c SotA \ubaa8\ub378\uc744 \uc7ac\ud604 \ub610\ub294 \uac1c\ubc1c\ud558\uace0, \ubaa8\ub378\uc744 \uc628\ub514\ubc14\uc774\uc2a4 \ubc0f \uc11c\ubc84 \ud658\uacbd\uc5d0 \ubc30\ud3ec\ud558\uace0, \uc774\ud6c4 \ubaa8\ub2c8\ud130\ub9c1\ud558\uba70 \uc9c0\uc18d\uc801\uc73c\ub85c \ubaa8\ub378\uc744 \uac1c\uc120\ud558\ub294 AI flywheel \uc744 \uad6c\ucd95\ud558\ub294 \ub4f1 \ub2e4\uc591\ud55c \uc5c5\ubb34\ub97c \uc218\ud589\ud569\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c \ubc31\uc5d4\ub4dc/\ud504\ub860\ud2b8\uc5d4\ub4dc/DevOps \uc5d4\uc9c0\ub2c8\uc5b4, \ub370\uc774\ud130 \ubd84\uc11d\uac00, PM \ub4f1 \ub2e4\uc591\ud55c \uc804\ubb38\uc870\uc9c1\uacfc \uc801\uadf9\uc801\uc73c\ub85c \ud611\uc5c5\ud558\uba70 \ub3c4\uc6c0\uc744 \ubc1b\uc2b5\ub2c8\ub2e4. \uc77c\ud558\ub294 \ubaa8\uc2b5\uc5d0 \ub300\ud55c \uc870\uae08 \ub354 \uc790\uc138\ud55c \uc774\uc57c\uae30\ub294 \ub2e4\uc74c\uc758 \ub0b4\uc6a9\uc744 \ucc38\uace0\ud558\uc2dc\uba74 \uc88b\uc2b5\ub2c8\ub2e4. AI in Social Discovery (Blending Research and Production) [How AI Lab Works] Head of AI - Shurain \uc778\ud130\ubdf0 \uc5f0\uad6c\uc758 \uacb0\uacfc\ubb3c\uc744 \uc815\ub9ac\ud558\uc5ec \ub17c\ubb38 \ud639\uc740 \ucf54\ub4dc\ub85c \uacf5\uac1c\ud558\ub294 \uac83 \ub610\ud55c \ud300 \ubaa9\ud45c \uc911 \ud558\ub098\uc785\ub2c8\ub2e4. \uc81c\ud488\uc5d0 \uc0ac\uc6a9\ud558\uae30 \uc704\ud55c \ubaa9\uc801\uc73c\ub85c \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc744 \ub9cc\ub4e4\ub2e4 \ubcf4\uba74, \uae30\uc874 \uc5f0\uad6c\ub85c\ub294 \ubd80\uc871\ud55c \uacbd\uc6b0\uac00 \ub9ce\uc2b5\ub2c8\ub2e4. \ubd80\uc871\ud55c \ubd80\ubd84\uc744 \ucc44\uc6b0\uae30 \uc704\ud574 \uc9c4\ud589\ub41c \uc5f0\uad6c\uc758 \uacb0\uacfc\ubb3c\uc744 \ud504\ub85c\uc81d\ud2b8 \ucc38\uc5ec\uc790\ub4e4\uc774 \ubaa8\ub450 \ud568\uaed8 \ud611\uc5c5\ud558\uc5ec \uc5f0\uad6c\uc758 \uc758\ubbf8 \uc788\ub294 \ubd80\ubd84\uc744 \uc815\ub3c8\ud558\uace0 \uac00\ub2a5\ud558\ub2e4\uba74 \ucf54\ub4dc\uc640 \ud568\uaed8 \uacf5\uac1c\ud569\ub2c8\ub2e4. \uadf8 \uacb0\uacfc, \uc9c0\uae08\uae4c\uc9c0 \uc544\ub798\uc640 \uac19\uc740 \ub300\uc678\uc801 \uc5f0\uad6c \uc131\uacfc\ub97c \uac70\ub458 \uc218 \uc788\uc5c8\uc2b5\ub2c8\ub2e4. 2023\ub144 TiDAL: \ud6a8\uc728\uc801\uc778 \ud559\uc2b5 \uacfc\uc815\uc758 \ubaa8\ub378 \ud589\ub3d9\uc5d0 \uae30\ubc18\ud55c \uc561\ud2f0\ube0c \ub7ec\ub2dd \uae30\ubc95 ICCV 2023 \uac8c\uc7ac 2023\ub144 \ubaa8\ub354\ub808\uc774\uc158 \ud658\uacbd\uc5d0\uc11c \uc5ec\ub7ec \ubd84\ub958 \uae30\uc900\uc744 \ub3d9\uc2dc\uc5d0 \ub9cc\uc871\ud558\uae30 \uc704\ud55c \ubb38\ud131\uac12\uc744 \uc7a1\ub294 \uc5f0\uad6c WSDM 2023 \uac8c\uc7ac 2022\ub144 \ub300\ud654 \uc0dd\uc131\uc5d0\uc11c\uc758 \uc758\ubbf8\uc801 \ub2e4\uc591\uc131\uc744 \ub192\uc774\ub294 \uc5f0\uad6c EMNLP 2022 \uac8c\uc7ac 2022\ub144 \ub808\uc774\ube14 \ub178\uc774\uc988\uac00 \uc2ec\ud55c \ud658\uacbd\uc5d0\uc11c \ud6a8\uacfc\uc801\uc73c\ub85c \ud559\uc2b5\ud558\ub294 \ubc29\ubc95 ECCV 2022 \uac8c\uc7ac 2022\ub144 \ud0c0\uae43 \uce90\ub9ad\ud130\uc758 \uba87\uac00\uc9c0 \ubc1c\ud654\ub9cc\uc744 \uc774\uc6a9\ud558\uc5ec \ud0c0\uae43 \uce90\ub9ad\ud130\ub97c \ubaa8\ubc29\ud558\ub294 \ucc57\ubd07\uc5f0\uad6c NAACL 2022 \uac8c\uc7ac 2022\ub144 \ub300\ud654 \uc0dd\uc131 \ubaa8\ub378\uc5d0\uc11c \uc608\uc2dc\ub97c \ud65c\uc6a9\ud558\uc5ec \uc131\ub2a5\uc744 \ub192\uc774\ub294 \uc5f0\uad6c ACL 2022 Workshop \ubc1c\ud45c 2022\ub144 \ubaa8\ubc14\uc77c \ud658\uacbd\uc5d0\uc11c \uc624\ub514\uc624 \ubd84\ub958\ub97c \uc704\ud55c distillation \uae30\uc220 \uc5f0\uad6c ICASSP \uac8c\uc7ac 2021\ub144 \ud074\ub9ad\ub960 \uc608\uce21\uc744 \uc704\ud55c \uc911\uc694\ub3c4 \ubcf4\uc874\uc774 \uac00\ub2a5\ud55c \ud53c\uccd0 \uc815\uaddc\ud654 \uc5f0\uad6c ICDM Workshop Best Paper \uc218\uc0c1 2021\ub144 Tabular Learning \uae30\ubc18 \ud6a8\uc728\uc801\uc778 Click-Through Rate Prediction \ubaa8\ub378 ICLR 2021 Workshop \ubc1c\ud45c 2021\ub144 \ud6a8\uc728\uc801\uc778 Retriever\uae30\ubc18 Chatbot\uc744 \uc704\ud55c Large-Scale Generative \ubaa8\ub378 \ud65c\uc6a9 \uc5f0\uad6c EMNLP 2021 \uac8c\uc7ac 2020\ub144 Long-tailed Visual Recognition \ubb38\uc81c\ub97c Label distribution shift \uad00\uc810\uc5d0\uc11c \ud574\uacb0\ud558\ub294 \uae30\uc220 CVPR 2021 \uac8c\uc7ac 2020\ub144 \ud4e8\uc0f7 \ub7ec\ub2dd\uc744 \ud1b5\ud55c Text-to-Speech(TTS) \uae30\uc220 INTERSPEECH 2020 \uac8c\uc7ac 2019\ub144 \ud4e8\uc0f7 \ub7ec\ub2dd\uc744 \ud1b5\ud55c \uc548\uba74 \uc7ac\ud604 \uae30\uc220 AAAI 2020 \uac8c\uc7ac 2019\ub144 \ubaa8\ubc14\uc77c\uc5d0\uc11c \ube60\ub974\uac8c \ub3d9\uc791\ud558\ub294 \ud0a4\uc6cc\ub4dc \uc2a4\ud31f\ud305 \ubaa8\ub378(TC-ResNet) INTERSPEECH 2019 \uac8c\uc7ac 2019\ub144 \ubaa8\ubc14\uc77c \ud658\uacbd\uc5d0 \ucd5c\uc801\ud654\ub41c \uacbd\ub7c9 \uc774\ubbf8\uc9c0 \uc138\uadf8\uba58\ud14c\uc774\uc158 \ubaa8\ub378(MMNet) \uc544\uce74\uc774\ube0c \uc5c5\ub85c\ub4dc 2018\ub144 \uc800\uc804\ub825 \uc774\ubbf8\uc9c0 \uc778\uc2dd \ub300\ud68c (LPIRC) 2\ub4f1 ML \uc5f0\uad6c\uac00 \uc798 \uc9c4\ud589\ub418\uae30 \uc704\ud574\uc11c\ub294 \ub525\ub7ec\ub2dd \ud559\uc2b5\uc744 \uc704\ud55c \uc778\ud504\ub77c\ub3c4 \uc798 \uac16\ucd94\uc5b4\uc838\uc57c\ud569\ub2c8\ub2e4. \ud558\uc774\ud37c\ucee4\ub125\ud2b8\uc5d0\uc11c\ub294 ML Engineer\ub4e4\uc774 \ucda9\ubd84\ud788 \ubaa8\ub378 \uac1c\ubc1c \ubc0f \uc2e4\ud5d8\uc744 \uc9c4\ud589\ud560 \uc218 \uc788\ub3c4\ub85d \uc790\uccb4\uc801\uc778 \ub525\ub7ec\ub2dd \uc5f0\uad6c\uc6a9 \ud074\ub7ec\uc2a4\ud130\ub97c \uad6c\ucd95\ud558\uc5ec \ud65c\uc6a9\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. DGX-A100 20\ub300\ub85c \uad6c\uc131\ub41c \ud074\ub7ec\uc2a4\ud130(\ucd1d 160\ub300\uc758 A100 GPU)\ub97c \ud3ec\ud568\ud55c \ub2e4\uc591\ud55c on-premise \uc7a5\ube44\ub4e4\uc744 \uc5f0\uad6c\uac1c\ubc1c\uc5d0 \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ucd94\uac00\uc801\uc73c\ub85c, \ud504\ub85c\ub355\uc158\uc744 \uc704\ud55c \ud30c\uc774\ud504\ub77c\uc778, \ub370\uc774\ud130 \uc218\uc9d1 \ubc0f \uc804\ucc98\ub9ac\uc640 \uc11c\ube59\uc740 Kubeflow pipeline\uc744 \ube44\ub86f\ud558\uc5ec BigQuery \ub4f1\uc744 \uc801\uadf9 \ud65c\uc6a9 \uc911\uc785\ub2c8\ub2e4. \ub610, ML \ubaa8\ub378\uc758 \uc81c\ud488\ud654\ub97c \ub3c4\uc640\uc8fc\uc2e4 \ub2e4\uc591\ud55c Software Engineer(\ubc31\uc5d4\ub4dc/\ud504\ub860\ud2b8\uc5d4\ub4dc/DevOps/MLSE)\ubd84\ub4e4\uacfc \ud568\uaed8 \uc77c\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc5c5\ubb34 \ub0b4\uc6a9 \ud558\uc774\ud37c\ucee4\ub125\ud2b8\ub294 \uc81c\ud488\uc5d0 \uba38\uc2e0\ub7ec\ub2dd \uae30\uc220\uc744 \uc801\uc6a9\ud558\uae30 \uc704\ud574 \ub2e4\uc591\ud55c \ubc29\uba74\uc73c\ub85c \ub178\ub825\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud558\uc774\ud37c\ucee4\ub125\ud2b8\uc758 ML Engineer\ub294 \ud06c\uac8c \ub2e4\uc74c\uacfc \uac19\uc740 3\uac00\uc9c0 \ubd84\uc57c \uc911 \ud55c \uac00\uc9c0 \ubd84\uc57c\uc5d0\uc11c \uc5c5\ubb34\ub97c \uc218\ud589\ud558\uac8c \ub429\ub2c8\ub2e4. [Recommendation] \uc81c\ud488\uc5d0 \ud3ec\ud568\ub418\ub294 \ub2e4\uc591\ud55c \ucd94\ucc9c \ubb38\uc81c\ub97c \ud574\uacb0\ud568\uc73c\ub85c\uc368 \uc0ac\uc6a9\uc790\ub4e4\uc5d0\uac8c \ubcf4\ub2e4 \ub098\uc740 \uacbd\ud5d8\uc744 \uc81c\uacf5\ud558\uace0, \uad81\uadf9\uc801\uc73c\ub85c \uc7a5\uae30 \ub9e4\ucd9c \ud5a5\uc0c1\uc5d0 \uae30\uc5ec\ud569\ub2c8\ub2e4. \ud568\uaed8 \ub2e4\uc74c\uacfc \uac19\uc740 \ubb38\uc81c\ub4e4\uc744 \ud574\uacb0\ud560 \uc218 \uc788\ub294 \ubd84\ub4e4\uc744 \ucc3e\uace0 \uc788\uc2b5\ub2c8\ub2e4. (\uc2a4\ucffc\ub4dc \uc778\ud130\ubdf0) \uc2e0\uaddc \uc0ac\uc6a9\uc790\uc5d0\uac8c \uc88b\uc740 \uacbd\ud5d8\uc744 \uc8fc\uae30 \uc704\ud55c cold-start \ucd94\ucc9c \ubb38\uc81c(session-based recommendation, graph-based recommendation, contextual bandit\uacfc \uac19\uc774 few-shot \ub370\uc774\ud130\ub9cc\uc73c\ub85c\ub3c4 \uc0ac\uc6a9\uc790\uc758 \uc120\ud638\ub97c \ud30c\uc545\ud560 \uc218 \uc788\ub294 \uc2dc\uc2a4\ud15c, \uc2e0\uaddc \uc0ac\uc6a9\uc790\uc5d0 \ub300\ud55c \ub370\uc774\ud130\uac00 \ubd80\uc871\ud560 \ub54c \uc2e0\uaddc \uc0ac\uc6a9\uc790\uc5d0 \ub300\ud55c \ucd94\ucc9c \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0a4\uae30 \uc704\ud55c \ud559\uc2b5 \ubc29\ubc95 \ub4f1) \uc591\ucabd \uc0ac\uc6a9\uc790\uac00 \ubaa8\ub450 \ub9cc\uc871\ud560 \uc218 \uc788\ub294 \uc0c1\ud638(reciprocal) \ucd94\ucc9c \ubb38\uc81c \uc2e4\uc2dc\uac04\uc73c\ub85c \ubcc0\uacbd\ub418\ub294 \ucd94\ucc9c \ud6c4\ubcf4\uad70\uc5d0 \ub300\ud574 \ub9e4\uc6b0 \ube60\ub978 \uc2dc\uac04 \uc548\uc5d0 \ucd94\ub860\uc744 \uc218\ud589\ud558\ub294 real-time \ucd94\ucc9c \ubb38\uc81c (session-based recommendation, graph-based recommendation, reinforcment learning, ) \uc5ec\ub7ec \ubaa9\ud45c \uc9c0\ud45c\ub4e4 \uc0ac\uc774\uc758 trade-off\ub97c \uace0\ub824\ud558\ub294 \ucd94\ucc9c \ubb38\uc81c \uc7a5\uae30 \uc9c0\ud45c\ub97c \ud5a5\uc0c1\uc2dc\ud0a4\ub294 1\ucc28 \ubaa9\ud45c \uc9c0\ud45c\ub97c \ucc3e\ub294 \ubb38\uc81c [Trust & Safety] \uc0ac\uc6a9\uc790\ub4e4\uc758 \ub9cc\uc871\uc2a4\ub7ec\uc6b4 \uacbd\ud5d8\uc744 \uc704\ud574 \ucf58\ud150\uce20\uac00 \uc5b4\ub5a4 \ub0b4\uc6a9\uc744 \ub2f4\uace0 \uc788\ub294\uc9c0 \uc774\ub97c \uc774\ud574\ud558\ub294 \ub2e4\uc591\ud55c \uae30\uc220 \ubc0f \uc774\ub7ec\ud55c \uc815\ubcf4\ub97c \ud65c\uc6a9\ud558\ub294 \uc5f0\uad6c \uac1c\ubc1c\uc744 \uc218\ud589\ud569\ub2c8\ub2e4. \uc601\uc0c1\uc774\ub098 \uc74c\uc131 \ubc0f \uc790\uc5f0\uc5b4\ub85c \uad6c\uc131\ub41c \ube44\uc815\ud615 \ub370\uc774\ud130\ub97c \uc785\ub825\uc73c\ub85c \ubc1b\uc544\ub4e4\uc5ec \uc758\uc0ac\uacb0\uc815\uc744 \ub0b4\ub9b4 \uc218 \uc788\ub3c4\ub85d \uc720\uc6a9\ud55c \uc815\ubcf4\ub97c \ucd94\ucd9c\ud558\uae30 \uc704\ud574\uc11c \ub2e4\uc74c\uacfc \uac19\uc740 \ubb38\uc81c\ub97c \ud568\uaed8 \ud480 \uc218 \uc788\ub294 \ubd84\ub4e4\uc744 \ucc3e\uace0 \uc788\uc2b5\ub2c8\ub2e4. (\uc2a4\ucffc\ub4dc \uc778\ud130\ubdf0) \ubaa8\ubc14\uc77c \ud658\uacbd\uc5d0\uc11c \ube60\ub978 \uc18d\ub3c4\ub97c \ub0bc \uc218 \uc788\ub294 \uacbd\ub7c9 \ubaa8\ub378\uacfc \ucd5c\uc801\ud654\uc5d0 \ub300\ud55c \ubb38\uc81c \ud6a8\uc728\uc801\uc774\uace0 label\uc758 \uc911\uc694\ub3c4\ub97c \uc870\uc808\ud560 \uc218 \uc788\ub294 multi-task \ud639\uc740 multi-label \ubaa8\ub378\uc5d0 \ub300\ud55c \ubb38\uc81c Partial multi-modal \ub370\uc774\ud130\ub97c \ud65c\uc6a9\ud558\ub294 \ubb38\uc81c \uc2a4\ud2b8\ub9bc\uc73c\ub85c \uc720\uc785\ub418\ub294 \uc0ac\uc6a9\uc790 \ud589\ub3d9 \ub85c\uadf8\uc640 content understanding \uacb0\uacfc\uc5d0 \uae30\ubc18\ud55c \uc2e4\uc2dc\uac04 \uc774\uc0c1 \uc0ac\uc6a9\uc790(ex. \uc2a4\ud338/\uac00\uc9dc \uacc4\uc815)\ub97c \ud0d0\uc9c0\ud558\ub294 \ubb38\uc81c Active learning\uc744 \ud1b5\ud55c \ud6a8\uc728\uc801\uc778 \ub370\uc774\ud130 \ub77c\ubca8\ub9c1 \ubc29\ubc95 \ud639\uc740 \ubaa8\ub378 \ud559\uc2b5\uc5d0 \ud544\uc694\ud55c \ub370\uc774\ud130\ub97c \uc904\uc77c \uc218 \uc788\ub294 core-set selection \ubc29\ubc95 [Generative AI] \ub2e4\uc591\ud55c \uc0dd\uc131\ud615 AI \uc5f0\uad6c \uac1c\ubc1c\uc744 \ud1b5\ud574 \uc0ac\uc6a9\uc790\ub4e4\uc5d0\uac8c \uc774\uc804\uc5d0 \uc5c6\ub358 \uc0c8\ub85c\uc6b4 \uacbd\ud5d8\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc11c\ube44\uc2a4 \ub0b4\uc5d0\uc11c \uc0ac\uc6a9\uc790\ub4e4\uc774 \uac1c\uc778\ud654\ub41c \ucee8\ud150\uce20\ub97c \uc27d\uac8c \uc0dd\uc131\ud558\uace0 \uc790\uae30 \ud45c\ud604\uc744 \ud560 \uc218 \uc788\ub294 \ub3c4\uad6c\ub97c \ub9cc\ub4e4\uba70, \uc0dd\uc131\ud615 AI\ub97c \ud65c\uc6a9\ud574 \uc0c8\ub85c\uc6b4 \uae30\ub2a5\uc744 \uac1c\ubc1c\ud569\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 \ud568\uaed8 \ub2e4\uc74c\uacfc \uac19\uc740 \ubb38\uc81c\ub97c \ud574\uacb0\ud560 \uc218 \uc788\ub294 \ubd84\ub4e4\uc744 \ucc3e\uace0 \uc788\uc2b5\ub2c8\ub2e4. (\uc2a4\ucffc\ub4dc \uc778\ud130\ubdf0) \uc0ac\uc6a9\uc790\uac00 \uc6d0\ud558\ub294 \ub300\uc0c1\uc758 \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\ub294 \uac1c\uc778\ud654\ub41c \uc774\ubbf8\uc9c0 \uc0dd\uc131 \ubaa8\ub378 \uac1c\ubc1c \ub300\uaddc\ubaa8 \uc5b8\uc5b4\ubaa8\ub378(Large Language Model)\uc744 \ud65c\uc6a9\ud55c \uc0c8\ub85c\uc6b4 \ud53c\uccd0 \uac1c\ubc1c, \uc774\ub97c \uc704\ud55c \ub300\uaddc\ubaa8 \uc5b8\uc5b4 \ubaa8\ub378 \ud559\uc2b5, \ud29c\ub2dd \ubc0f \uc11c\ube59 \ud070 \uaddc\ubaa8\uc758 \uc0dd\uc131\ud615 \ubaa8\ub378\uc774 \ub300\uc6a9\ub7c9 \ud2b8\ub798\ud53d\uc744 \uc548\uc815\uc801\uc73c\ub85c \ucc98\ub9ac\ud560 \uc218 \uc788\ub3c4\ub85d \ubaa8\ub378 \uac1c\ubc1c \ubc0f \ucd5c\uc801\ud654 \uc0dd\uc131\ud615 \ubaa8\ub378\uc744 \ud65c\uc6a9\ud558\uc5ec \uc11c\ube44\uc2a4 \ub0b4 \uc0ac\uc6a9\uc790 \uacbd\ud5d8\uc744 \ud601\uc2e0\ud560 \uc218 \uc788\ub294 \ubc29\ubc95\uc5d0 \ub300\ud55c \uc5f0\uad6c\uc640 \uace0\ubbfc [\uacf5\ud1b5] \uacf5\ud1b5\uc801\uc73c\ub85c, \uc81c\ud488\uc5d0 \ud3ec\ud568\ub418\ub294 AI \uae30\uc220\uc744 \uc5f0\uad6c\ud558\uae30 \uc704\ud55c \ub178\ub825\ub4e4\ub3c4 \uafb8\uc900\ud788 \uc9c4\ud589\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc2e4\uc81c \ud504\ub85c\ub355\uc158 \ud658\uacbd\uc5d0\uc11c\ub294 Kaggle\uacfc \uac19\uc740 \uc815\uc81c\ub41c \ub370\uc774\ud130\uc14b\uc774 \uc874\uc7ac\ud558\uc9c0 \uc54a\uc73c\uba70, \ub300\ubd80\ubd84\uc758 \uacbd\uc6b0 \ub9e4\uc77c \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\uac00 \uc2dc\uc2a4\ud15c\uc5d0 \uc720\uc785\ub429\ub2c8\ub2e4. \uc5b4\uc81c\ubcf4\ub2e4 \uc624\ub298 \ub354 \ub098\uc740 \ubaa8\ub378\uc744 \uc790\ub3d9\uc73c\ub85c \uc0dd\uc131\ud558\ub294 Flywheel\uc744 \uad6c\ucd95\ud558\uae30 \uc704\ud574, \ud568\uaed8 \ub2e4\uc74c\uacfc \uac19\uc740 \ubb38\uc81c\ub97c \ud574\uacb0\ud560 \uc218 \uc788\ub294 \ubd84\ub4e4\uc744 \ucc3e\uace0 \uc788\uc2b5\ub2c8\ub2e4. Highly imbalanced \ub610\ub294 noisy label \ub370\uc774\ud130\ub97c \ub2e4\ub8e8\ub294 \ubc29\ubc95 \uae30\uc874\uc5d0 deploy\ub41c \ubaa8\ub378\uc744 \uc9c0\uc18d\uc801\ud574\uc11c \uac1c\uc120\ud560 \uc218 \uc788\ub294 continual/life-long learning \ubc29\ubc95 \ubaa8\ub378 task \uc694\uad6c\uc0ac\ud56d\uc758 \ubcc0\ud654\uc640 \uc2e0\uaddc \uc11c\ube44\uc2a4\uc5d0 \ub300\uc751\ud560 \uc218 \uc788\ub294 meta-learning \ubc29\ubc95 Large scale model\uc744 \ud559\uc2b5\ud558\uace0, \uc2e4\uc81c \uc11c\ube44\uc2a4 \ud658\uacbd\uc5d0\uc11c \ucd08\ub2f9 \uc218\ubc31 \ub610\ub294 \uc218\ucc9c \uac1c\uc758 \uc785\ub825\uc744 \uc548\uc815\uc801\uc73c\ub85c \ucc98\ub9ac\ud560 \uc218 \uc788\ub3c4\ub85d \ud560 \uc218 \uc788\ub294 modeling, optimization, distillation \ubc29\ubc95 \uc9c0\uc6d0 \uc790\uaca9 AI/ML \ub3c4\uba54\uc778 \uc804\ubc18\uc5d0 \ub300\ud55c \uc774\ud574\uc640 \uc801\uc5b4\ub3c4 \ud55c \uac1c \uc774\uc0c1\uc758 \ud2b9\uc815 \ub3c4\uba54\uc778\uc5d0 \ub300\ud55c \uae4a\uc774 \uc788\ub294 \uc9c0\uc2dd\uc744 \uac16\ucd94\uace0, 3\ub144 \uc774\uc0c1\uc758 \uad00\ub828 \ud504\ub85c\uc81d\ud2b8 \uacbd\ud5d8\uc774 \uc788\uc73c\uc2e0 \ubd84 \uc9c0\uc18d\uc801\uc778 \uc8fc\ub3c4\uc801 \ud559\uc2b5\uc744 \ud1b5\ud574 \ud300\uc758 AI/ML\uc744 \ud3ec\ud568\ud55c \uae30\uc220\uc801 \uacbd\uc7c1\ub825\uc744 \ub9cc\ub4e4\uace0 \uc720\uc9c0\ud558\uac8c \ub3c4\uc640\uc8fc\uc2e4 \uc218 \uc788\ub294 \ubd84 \ubcf4\ud1b5\uc758 \ubc29\ubc95\uc73c\ub85c \ud480 \uc218 \uc5c6\ub294 \uc5d4\uc9c0\ub2c8\uc5b4\ub9c1 \uc81c\uc57d \uc870\uac74\uc744 AI \ubaa8\ub378\ub9c1 \ub2a5\ub825\uacfc \uc18c\ud504\ud2b8\uc6e8\uc5b4 \uc5d4\uc9c0\ub2c8\uc5b4\ub9c1 \uc804\ubc18\uc5d0 \ub300\ud55c \uae4a\uc740 \uc218\uc900\uc758 \uc774\ud574\ub97c \ubc14\ud0d5\uc73c\ub85c \ud574\uacb0\ud560 \uc218 \uc788\ub294 \uc5ed\ub7c9\uc744 \uac16\ucd98 \ubd84 \uc2e4\uc81c \uc11c\ube44\uc2a4\uc5d0 AI \uae30\uc220\uc744 \ud1b5\ud569\ud558\uace0 \uc8fc\uc694 \uc9c0\ud45c\ub97c \uc720\uc758\ubbf8\ud558\uac8c \ud5a5\uc0c1\uc2dc\ucf1c \ubcf8 \uacbd\ud5d8\uc774 \uc788\uc73c\uc2e0 \ubd84 ML \ubaa8\ub378\uc744 \ud559\uc2b5\ud558\uace0 \uc2e4\uc81c \uc11c\ube44\uc2a4\uc5d0 \ubc30\ud3ec\ud558\uae30 \uc704\ud574, \uc5ec\ub7ec \uc9c1\uad70\uc758 \uc774\ud574\uad00\uacc4\uc790\uc640 \ud611\uc5c5\ud560 \uc218 \uc788\ub294 \uac15\ub825\ud55c \ucee4\ubba4\ub2c8\ucf00\uc774\uc158 \ub2a5\ub825\uacfc \uc5d4\uc9c0\ub2c8\uc5b4\ub9c1 \uc5ed\ub7c9\uc744 \uac16\ucd94\uc2e0 \ubd84 \uad6c\ud604\uccb4\uac00 \uacf5\uac1c\ub418\uc5b4\uc788\uc9c0 \uc54a\uc740 \ub17c\ubb38\uc744 \uc2a4\ud06c\ub798\uce58\ubd80\ud130 \ube60\ub974\uace0 \uc815\ud655\ud558\uac8c \uad6c\ud604\ud574 \ubcf8 \uacbd\ud5d8\uc774 \uc788\uc73c\uc2e0 \ubd84 \ub2e4\ub978 ML \uad00\ub828 \uc9c1\uad70 \uc5d4\uc9c0\ub2c8\uc5b4 \uc758 \uc131\uc7a5\uc744 \ub3c4\uc640\ubcf8 \uacbd\ud5d8\uc774 \uc788\uac70\ub098, \uad00\ub828\ud55c \uc5ed\ub7c9\uc744 \uac16\ucd94\uc2e0 \ubd84 \ud559\uc704\ub098 \uad6d\uc801\uc740 \ubb34\uad00\ud558\ub418, \ud55c\uad6d\uc5b4\ub85c \uc6d0\ud65c\ud55c \uc758\uc0ac\uc18c\ud1b5\uc774 \uac00\ub2a5\ud55c \ubd84 \uc6b0\ub300 \uc0ac\ud56d \uae30\uacc4\ud559\uc2b5 \uad00\ub828 \ud0d1\ud2f0\uc5b4 \ud559\ud68c \ubc0f \uc800\ub110(NeurIPS, ICLR, ICML, CVPR, ICCV/ECCV, KDD, ) \uac8c\uc7ac \uc2e4\uc801 \ud639\uc740 AI \uad00\ub828 \ub300\ud68c \uc218\uc0c1 \uc2e4\uc801\uc774 \uc788\uc73c\uc2e0 \ubd84 \uacf5\uac1c\ub41c \ubca4\uce58\ub9c8\ud06c \ub370\uc774\ud130 \uc14b\uc5d0\uc11c SotA\ub97c \ucc0d\uc5b4\ubcf8 \uacbd\ud5d8\uc774 \uc788\uc73c\uc2e0 \ubd84 \ud074\ub77c\uc774\uc5b8\ud2b8(Android, iOS), \ubc31\uc5d4\ub4dc\ub97c \ud3ec\ud568\ud574 AI/ML \ubd84\uc57c \uc678 \uac1c\ubc1c \uacbd\ud5d8\uc774 \ud48d\ubd80\ud558\uc2e0 \ubd84 \uae30\uacc4\ud559\uc2b5 \uad00\ub828 \uc624\ud508 \uc18c\uc2a4 \uac1c\ubc1c\uc5d0 \ucc38\uc5ec\ud574 \ubcf8 \uacbd\ud5d8\uc774 \uc788\uc73c\uc2e0 \ubd84 AI/ML \ub3c4\uba54\uc778 \uc804\ubc18\uc5d0 \ub300\ud55c \ubc29\ub300\ud55c \uc9c0\uc2dd\uc744 \uc790\ub791\ud560 \uc218 \uc788\uc73c\uc2e0 \ubd84 A/B \ud14c\uc2a4\ud2b8 \uc2e4\ud5d8 \uae30\ud68d \ubc0f \ud0c0\uac9f KPI \uc9c0\ud45c\ub97c \uc815\uc758\ud558\uace0, SQL\uae30\ubc18 \ub370\uc774\ud130 \ubd84\uc11d\uc744 \uc9c4\ud589\ud55c \uacbd\ud5d8\uc774 \uc788\uc73c\uc2e0 \ubd84 \uc778\uacfc\uad00\uacc4 \ubd84\uc11d \ubc0f \uae30\ud0c0 \ud1b5\uacc4 \uae30\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130\uc5d0\uc11c \uc758\ubbf8 \uc788\ub294 \ud1b5\ucc30\ub825\uc744 \ubc1c\uad74\ud558\uace0 \uc758\uc0ac\uacb0\uc815\uc5d0 \ud65c\uc6a9\ud574\ubcf8 \uacbd\ud5d8\uc774 \uc788\uc73c\uc2e0 \ubd84 \uc5d4\uc9c0\ub2c8\uc5b4\ub9c1 \ud300\uc744 \ub9ac\ub4dc\ud574\ubcf8 \uacbd\ud5d8\uc774 \uc788\uc73c\uc2e0 \ubd84 \uc601\uc5b4\uc5d0 \ub2a5\ud1b5\ud558\uc2e0 \ubd84 $ - $ a year None \ud558\uc774\ud37c\ucee4\ub125\ud2b8\uac00 \ucc44\uc6a9\ud558\ub294 \ud3ec\uc9c0\uc158\uc5d0 \uc9c0\uc6d0\ud558\ub294 \uacbd\uc6b0, \uac1c\uc778\uc815\ubcf4 \ucc98\ub9ac\uc5d0 \uad00\ud558\uc5ec\uc11c\ub294 \ubcf8 \uac1c\uc778\uc815\ubcf4\ucc98\ub9ac\ubc29\uce68\uc774 \uc801\uc6a9\ub429\ub2c8\ub2e4: https://career.hyperconnect.com/privacy", "stars": 0.0, "medallionProgram": null, "location_flexibility": null, "work_location_option": "onsite", "canonicalPositionUrl": "https://join.matchgroupcareers.com/careers/job/22203755", "isPrivate": false}, {"id": 22203760, "name": "Machine Learning Engineer", "location": "Seoul", "locations": ["Seoul"], "hot": 0, "department": "Hyperconnect", "business_unit": "AI/ML \uc9c1\uad70 \uc9d1\uc911\ucc44\uc6a9", "t_update": 1714639978, "t_create": 1714611346, "ats_job_id": "9ab7b604-0a12-4ef7-b9c1-66db63e4a38d", "display_job_id": "9ab7b604-0a12-4ef7-b9c1-66db63e4a38d", "type": "ATS", "id_locale": "6387603490200177454-en", "job_description": "\ud558\uc774\ud37c\ucee4\ub125\ud2b8 Machine Learning Engineer (ML Engineer) \ub294 \uc0ac\ub78c\uacfc \uc0ac\ub78c \uc0ac\uc774\ub97c \uc5f0\uacb0\ud558\ub294 \uc11c\ube44\uc2a4\uc5d0\uc11c, \uae30\uc874\uc758 \uae30\uc220\ub85c\ub294 \uc811\uadfc\ud558\uae30 \uc5b4\ub835\uc9c0\ub9cc \uba38\uc2e0\ub7ec\ub2dd \uae30\uc220\uc744 \ud1b5\ud574 \ud480 \uc218 \uc788\ub294 \ubb38\uc81c\ub4e4\uc744 \ucc3e\uc544\ub0b4\uace0 \ud574\uacb0\ud558\uc5ec \uc0ac\uc6a9\uc790 \uacbd\ud5d8\uc744 \ud601\uc2e0\ud569\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 \uc601\uc0c1/\uc74c\uc131/\uc790\uc5f0\uc5b4/\ucd94\ucc9c \ub4f1\uc744 \ud3ec\ud568\ud558\uc5ec \ub2e4\uc591\ud55c \ub3c4\uba54\uc778\uc758 \uc218\ub9ce\uc740 \ubaa8\ub378\uc744 \uac1c\ubc1c\ud558\uace0, \ubaa8\ubc14\uc77c \ubc0f \ud074\ub77c\uc6b0\ub4dc \uc11c\ubc84\ub97c \ud1b5\ud574 \uc548\uc815\uc801\uc73c\ub85c \uc81c\uacf5\ud558\uba74\uc11c \ub9c8\uc8fc\ud558\ub294 \uc5f0\uad6c \uc8fc\uc81c\ub4e4\uc744 \ud480\uc5b4\ub0b4\uc5b4 \uc6b0\ub9ac\uac00 \ub9cc\ub4e4\uc5b4 \ub0b4\ub294 \uae30\uc220\uc774 \uc2e4\uc81c \uc11c\ube44\uc2a4\uc758 \uc131\uc7a5\uc5d0 \uae30\uc5ec\ud558\ub294 \uac83\uc744 \ubaa9\ud45c\ub85c \ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubaa9\ud45c \uc544\ub798 \ud558\uc774\ud37c\ucee4\ub125\ud2b8\uc758 ML Engineer\ub294 \uc544\uc790\ub974, \ud558\ucfe0\ub098 \ub4f1 \ud558\uc774\ud37c\ucee4\ub125\ud2b8\uc758 \uc5ec\ub7ec \uc81c\ud488\ub4e4\uc5d0 \uae30\uc5ec\ud558\ub294 \uba38\uc2e0\ub7ec\ub2dd \uae30\uc220\ub4e4\uc744 \uc218\ub144\uac04 \ubc1c\uc804\uc2dc\ucf1c \ub098\uac00\uace0 \uc788\uc73c\uba70, \uc774\ub807\uac8c \ucd95\uc801\ub41c \uae30\uc220\ub4e4\uc744 \ub2e4\uc591\ud55c \uae00\ub85c\ubc8c \ube44\uc988\ub2c8\uc2a4 \uc11c\ube44\uc2a4\uc5d0\ub3c4 \uc190\uc27d\uac8c \ud65c\uc6a9\ud558\uae30 \uc704\ud55c \ubc29\uc548\uc744 \uc5f0\uad6c\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. ML Engineer\ub294 \ucd5c\ucca8\ub2e8\uc758 \ubaa8\ub378\uc744 \uc5f0\uad6c\ud558\uace0 \uac1c\uc120\ud558\ub294 \uacfc\ud559\uc790\ub85c\uc11c\uc758 \uc5f0\uad6c \ub2a5\ub825\uacfc, \ub9cc\ub4e4\uc5b4\uc9c4 \ubaa8\ub378\uc758 \uc2dc\uac04/\uacf5\uac04\uc801 \ubcf5\uc7a1\ub3c4\ub97c \uace0\ub824\ud574 \ucd94\ub860 \uc131\ub2a5\uc744 \uadf9\ud55c\uc73c\ub85c \ub04c\uc5b4\uc62c\ub9ac\ub294 \uacf5\ud559\uc790\ub85c\uc11c\uc758 \uac1c\ubc1c \ub2a5\ub825\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc774\ub7f0 \ub2a5\ub825\uc744 \ubc14\ud0d5\uc73c\ub85c \uc2e4\uc81c \uc11c\ube44\uc2a4\uc5d0\uc11c \uacaa\ub294 \ubb38\uc81c\ub97c \ubc1c\uacac/\uc815\uc758\ud558\uace0, \ubb38\uc81c\ud574\uacb0\uc744 \uc704\ud55c SotA \ubaa8\ub378\uc744 \uc7ac\ud604 \ub610\ub294 \uac1c\ubc1c\ud558\uace0, \ubaa8\ub378\uc744 \uc628\ub514\ubc14\uc774\uc2a4 \ubc0f \uc11c\ubc84 \ud658\uacbd\uc5d0 \ubc30\ud3ec\ud558\uace0, \uc774\ud6c4 \ubaa8\ub2c8\ud130\ub9c1\ud558\uba70 \uc9c0\uc18d\uc801\uc73c\ub85c \ubaa8\ub378\uc744 \uac1c\uc120\ud558\ub294 AI flywheel \uc744 \uad6c\ucd95\ud558\ub294 \ub4f1 \ub2e4\uc591\ud55c \uc5c5\ubb34\ub97c \uc218\ud589\ud569\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c \ubc31\uc5d4\ub4dc/\ud504\ub860\ud2b8\uc5d4\ub4dc/DevOps \uc5d4\uc9c0\ub2c8\uc5b4, \ub370\uc774\ud130 \ubd84\uc11d\uac00, PM \ub4f1 \ub2e4\uc591\ud55c \uc804\ubb38\uc870\uc9c1\uacfc \uc801\uadf9\uc801\uc73c\ub85c \ud611\uc5c5\ud558\uba70 \ub3c4\uc6c0\uc744 \ubc1b\uc2b5\ub2c8\ub2e4. \uc77c\ud558\ub294 \ubaa8\uc2b5\uc5d0 \ub300\ud55c \uc870\uae08 \ub354 \uc790\uc138\ud55c \uc774\uc57c\uae30\ub294 \ub2e4\uc74c\uc758 \ub0b4\uc6a9\uc744 \ucc38\uace0\ud558\uc2dc\uba74 \uc88b\uc2b5\ub2c8\ub2e4. AI in Social Discovery (Blending Research and Production) [How AI Lab Works] Head of AI - Shurain \uc778\ud130\ubdf0 \uc5f0\uad6c\uc758 \uacb0\uacfc\ubb3c\uc744 \uc815\ub9ac\ud558\uc5ec \ub17c\ubb38 \ud639\uc740 \ucf54\ub4dc\ub85c \uacf5\uac1c\ud558\ub294 \uac83 \ub610\ud55c \ud300 \ubaa9\ud45c \uc911 \ud558\ub098\uc785\ub2c8\ub2e4. \uc81c\ud488\uc5d0 \uc0ac\uc6a9\ud558\uae30 \uc704\ud55c \ubaa9\uc801\uc73c\ub85c \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc744 \ub9cc\ub4e4\ub2e4 \ubcf4\uba74, \uae30\uc874 \uc5f0\uad6c\ub85c\ub294 \ubd80\uc871\ud55c \uacbd\uc6b0\uac00 \ub9ce\uc2b5\ub2c8\ub2e4. \ubd80\uc871\ud55c \ubd80\ubd84\uc744 \ucc44\uc6b0\uae30 \uc704\ud574 \uc9c4\ud589\ub41c \uc5f0\uad6c\uc758 \uacb0\uacfc\ubb3c\uc744 \ud504\ub85c\uc81d\ud2b8 \ucc38\uc5ec\uc790\ub4e4\uc774 \ubaa8\ub450 \ud568\uaed8 \ud611\uc5c5\ud558\uc5ec \uc5f0\uad6c\uc758 \uc758\ubbf8 \uc788\ub294 \ubd80\ubd84\uc744 \uc815\ub3c8\ud558\uace0 \uac00\ub2a5\ud558\ub2e4\uba74 \ucf54\ub4dc\uc640 \ud568\uaed8 \uacf5\uac1c\ud569\ub2c8\ub2e4. \uadf8 \uacb0\uacfc, \uc9c0\uae08\uae4c\uc9c0 \uc544\ub798\uc640 \uac19\uc740 \ub300\uc678\uc801 \uc5f0\uad6c \uc131\uacfc\ub97c \uac70\ub458 \uc218 \uc788\uc5c8\uc2b5\ub2c8\ub2e4. 2023\ub144 TiDAL: \ud6a8\uc728\uc801\uc778 \ud559\uc2b5 \uacfc\uc815\uc758 \ubaa8\ub378 \ud589\ub3d9\uc5d0 \uae30\ubc18\ud55c \uc561\ud2f0\ube0c \ub7ec\ub2dd \uae30\ubc95 ICCV 2023 \uac8c\uc7ac 2023\ub144 \ubaa8\ub354\ub808\uc774\uc158 \ud658\uacbd\uc5d0\uc11c \uc5ec\ub7ec \ubd84\ub958 \uae30\uc900\uc744 \ub3d9\uc2dc\uc5d0 \ub9cc\uc871\ud558\uae30 \uc704\ud55c \ubb38\ud131\uac12\uc744 \uc7a1\ub294 \uc5f0\uad6c WSDM 2023 \uac8c\uc7ac 2022\ub144 \ub300\ud654 \uc0dd\uc131\uc5d0\uc11c\uc758 \uc758\ubbf8\uc801 \ub2e4\uc591\uc131\uc744 \ub192\uc774\ub294 \uc5f0\uad6c EMNLP 2022 \uac8c\uc7ac 2022\ub144 \ub808\uc774\ube14 \ub178\uc774\uc988\uac00 \uc2ec\ud55c \ud658\uacbd\uc5d0\uc11c \ud6a8\uacfc\uc801\uc73c\ub85c \ud559\uc2b5\ud558\ub294 \ubc29\ubc95 ECCV 2022 \uac8c\uc7ac 2022\ub144 \ud0c0\uae43 \uce90\ub9ad\ud130\uc758 \uba87\uac00\uc9c0 \ubc1c\ud654\ub9cc\uc744 \uc774\uc6a9\ud558\uc5ec \ud0c0\uae43 \uce90\ub9ad\ud130\ub97c \ubaa8\ubc29\ud558\ub294 \ucc57\ubd07\uc5f0\uad6c NAACL 2022 \uac8c\uc7ac 2022\ub144 \ub300\ud654 \uc0dd\uc131 \ubaa8\ub378\uc5d0\uc11c \uc608\uc2dc\ub97c \ud65c\uc6a9\ud558\uc5ec \uc131\ub2a5\uc744 \ub192\uc774\ub294 \uc5f0\uad6c ACL 2022 Workshop \ubc1c\ud45c 2022\ub144 \ubaa8\ubc14\uc77c \ud658\uacbd\uc5d0\uc11c \uc624\ub514\uc624 \ubd84\ub958\ub97c \uc704\ud55c distillation \uae30\uc220 \uc5f0\uad6c ICASSP \uac8c\uc7ac 2021\ub144 \ud074\ub9ad\ub960 \uc608\uce21\uc744 \uc704\ud55c \uc911\uc694\ub3c4 \ubcf4\uc874\uc774 \uac00\ub2a5\ud55c \ud53c\uccd0 \uc815\uaddc\ud654 \uc5f0\uad6c ICDM Workshop Best Paper \uc218\uc0c1 2021\ub144 Tabular Learning \uae30\ubc18 \ud6a8\uc728\uc801\uc778 Click-Through Rate Prediction \ubaa8\ub378 ICLR 2021 Workshop \ubc1c\ud45c 2021\ub144 \ud6a8\uc728\uc801\uc778 Retriever\uae30\ubc18 Chatbot\uc744 \uc704\ud55c Large-Scale Generative \ubaa8\ub378 \ud65c\uc6a9 \uc5f0\uad6c EMNLP 2021 \uac8c\uc7ac 2020\ub144 Long-tailed Visual Recognition \ubb38\uc81c\ub97c Label distribution shift \uad00\uc810\uc5d0\uc11c \ud574\uacb0\ud558\ub294 \uae30\uc220 CVPR 2021 \uac8c\uc7ac 2020\ub144 \ud4e8\uc0f7 \ub7ec\ub2dd\uc744 \ud1b5\ud55c Text-to-Speech(TTS) \uae30\uc220 INTERSPEECH 2020 \uac8c\uc7ac 2019\ub144 \ud4e8\uc0f7 \ub7ec\ub2dd\uc744 \ud1b5\ud55c \uc548\uba74 \uc7ac\ud604 \uae30\uc220 AAAI 2020 \uac8c\uc7ac 2019\ub144 \ubaa8\ubc14\uc77c\uc5d0\uc11c \ube60\ub974\uac8c \ub3d9\uc791\ud558\ub294 \ud0a4\uc6cc\ub4dc \uc2a4\ud31f\ud305 \ubaa8\ub378(TC-ResNet) INTERSPEECH 2019 \uac8c\uc7ac 2019\ub144 \ubaa8\ubc14\uc77c \ud658\uacbd\uc5d0 \ucd5c\uc801\ud654\ub41c \uacbd\ub7c9 \uc774\ubbf8\uc9c0 \uc138\uadf8\uba58\ud14c\uc774\uc158 \ubaa8\ub378(MMNet) \uc544\uce74\uc774\ube0c \uc5c5\ub85c\ub4dc 2018\ub144 \uc800\uc804\ub825 \uc774\ubbf8\uc9c0 \uc778\uc2dd \ub300\ud68c (LPIRC) 2\ub4f1 ML \uc5f0\uad6c\uac00 \uc798 \uc9c4\ud589\ub418\uae30 \uc704\ud574\uc11c\ub294 \ub525\ub7ec\ub2dd \ud559\uc2b5\uc744 \uc704\ud55c \uc778\ud504\ub77c\ub3c4 \uc798 \uac16\ucd94\uc5b4\uc838\uc57c \ud569\ub2c8\ub2e4. \ud558\uc774\ud37c\ucee4\ub125\ud2b8\uc5d0\uc11c\ub294 ML Engineer\ub4e4\uc774 \ucda9\ubd84\ud788 \ubaa8\ub378 \uac1c\ubc1c \ubc0f \uc2e4\ud5d8\uc744 \uc9c4\ud589\ud560 \uc218 \uc788\ub3c4\ub85d \uc790\uccb4\uc801\uc778 \ub525\ub7ec\ub2dd \uc5f0\uad6c\uc6a9 \ud074\ub7ec\uc2a4\ud130\ub97c \uad6c\ucd95\ud558\uc5ec \ud65c\uc6a9\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. DGX-A100 20\ub300\ub85c \uad6c\uc131\ub41c \ud074\ub7ec\uc2a4\ud130(\ucd1d 160\ub300\uc758 A100 GPU)\ub97c \ud3ec\ud568\ud55c \ub2e4\uc591\ud55c on-premise \uc7a5\ube44\ub4e4\uc744 \uc5f0\uad6c\uac1c\ubc1c\uc5d0 \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ucd94\uac00\uc801\uc73c\ub85c, \ud504\ub85c\ub355\uc158\uc744 \uc704\ud55c \ud30c\uc774\ud504\ub77c\uc778, \ub370\uc774\ud130 \uc218\uc9d1 \ubc0f \uc804\ucc98\ub9ac\uc640 \uc11c\ube59\uc740 Kubeflow pipeline\uc744 \ube44\ub86f\ud558\uc5ec BigQuery \ub4f1\uc744 \uc801\uadf9 \ud65c\uc6a9 \uc911\uc785\ub2c8\ub2e4. \ub610, ML \ubaa8\ub378\uc758 \uc81c\ud488\ud654\ub97c \ub3c4\uc640\uc8fc\uc2e4 \ub2e4\uc591\ud55c Software Engineer(\ubc31\uc5d4\ub4dc/\ud504\ub860\ud2b8\uc5d4\ub4dc/DevOps/MLSE)\ubd84\ub4e4\uacfc \ud568\uaed8 \uc77c\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc5c5\ubb34 \ub0b4\uc6a9 \ud558\uc774\ud37c\ucee4\ub125\ud2b8\ub294 \uc81c\ud488\uc5d0 \uba38\uc2e0\ub7ec\ub2dd \uae30\uc220\uc744 \uc801\uc6a9\ud558\uae30 \uc704\ud574 \ub2e4\uc591\ud55c \ubc29\uba74\uc73c\ub85c \ub178\ub825\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud558\uc774\ud37c\ucee4\ub125\ud2b8\uc758 ML Engineer\ub294 \ud06c\uac8c \ub2e4\uc74c\uacfc \uac19\uc740 3\uac00\uc9c0 \ubd84\uc57c \uc911 \ud55c \uac00\uc9c0 \ubd84\uc57c\uc5d0\uc11c \uc5c5\ubb34\ub97c \uc218\ud589\ud558\uac8c \ub429\ub2c8\ub2e4. [Recommendation] \uc81c\ud488\uc5d0 \ud3ec\ud568\ub418\ub294 \ub2e4\uc591\ud55c \ucd94\ucc9c \ubb38\uc81c\ub97c \ud574\uacb0\ud568\uc73c\ub85c\uc368 \uc0ac\uc6a9\uc790\ub4e4\uc5d0\uac8c \ubcf4\ub2e4 \ub098\uc740 \uacbd\ud5d8\uc744 \uc81c\uacf5\ud558\uace0, \uad81\uadf9\uc801\uc73c\ub85c \uc7a5\uae30 \ub9e4\ucd9c \ud5a5\uc0c1\uc5d0 \uae30\uc5ec\ud569\ub2c8\ub2e4. \ud568\uaed8 \ub2e4\uc74c\uacfc \uac19\uc740 \ubb38\uc81c\ub4e4\uc744 \ud574\uacb0\ud560 \uc218 \uc788\ub294 \ubd84\ub4e4\uc744 \ucc3e\uace0 \uc788\uc2b5\ub2c8\ub2e4. (\uc2a4\ucffc\ub4dc \uc778\ud130\ubdf0). \uc2e0\uaddc \uc0ac\uc6a9\uc790\uc5d0\uac8c \uc88b\uc740 \uacbd\ud5d8\uc744 \uc8fc\uae30 \uc704\ud55c cold-start \ucd94\ucc9c \ubb38\uc81c(session-based recommendation, graph-based recommendation, contextual bandit\uacfc \uac19\uc774 few-shot \ub370\uc774\ud130\ub9cc\uc73c\ub85c\ub3c4 \uc0ac\uc6a9\uc790\uc758 \uc120\ud638\ub97c \ud30c\uc545\ud560 \uc218 \uc788\ub294 \uc2dc\uc2a4\ud15c, \uc2e0\uaddc \uc0ac\uc6a9\uc790\uc5d0 \ub300\ud55c \ub370\uc774\ud130\uac00 \ubd80\uc871\ud560 \ub54c \uc2e0\uaddc \uc0ac\uc6a9\uc790\uc5d0 \ub300\ud55c \ucd94\ucc9c \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0a4\uae30 \uc704\ud55c \ud559\uc2b5 \ubc29\ubc95 \ub4f1) \uc591\ucabd \uc0ac\uc6a9\uc790\uac00 \ubaa8\ub450 \ub9cc\uc871\ud560 \uc218 \uc788\ub294 \uc0c1\ud638(reciprocal) \ucd94\ucc9c \ubb38\uc81c \uc2e4\uc2dc\uac04\uc73c\ub85c \ubcc0\uacbd\ub418\ub294 \ucd94\ucc9c \ud6c4\ubcf4\uad70\uc5d0 \ub300\ud574 \ub9e4\uc6b0 \ube60\ub978 \uc2dc\uac04 \uc548\uc5d0 \ucd94\ub860\uc744 \uc218\ud589\ud558\ub294 real-time \ucd94\ucc9c \ubb38\uc81c (session-based recommendation, graph-based recommendation, reinforcment learning, ) \uc5ec\ub7ec \ubaa9\ud45c \uc9c0\ud45c\ub4e4 \uc0ac\uc774\uc758 trade-off\ub97c \uace0\ub824\ud558\ub294 \ucd94\ucc9c \ubb38\uc81c \uc7a5\uae30 \uc9c0\ud45c\ub97c \ud5a5\uc0c1\uc2dc\ud0a4\ub294 1\ucc28 \ubaa9\ud45c \uc9c0\ud45c\ub97c \ucc3e\ub294 \ubb38\uc81c [Trust & Safety] \uc0ac\uc6a9\uc790\ub4e4\uc758 \ub9cc\uc871\uc2a4\ub7ec\uc6b4 \uacbd\ud5d8\uc744 \uc704\ud574 \ucf58\ud150\uce20\uac00 \uc5b4\ub5a4 \ub0b4\uc6a9\uc744 \ub2f4\uace0 \uc788\ub294\uc9c0 \uc774\ub97c \uc774\ud574\ud558\ub294 \ub2e4\uc591\ud55c \uae30\uc220 \ubc0f \uc774\ub7ec\ud55c \uc815\ubcf4\ub97c \ud65c\uc6a9\ud558\ub294 \uc5f0\uad6c \uac1c\ubc1c\uc744 \uc218\ud589\ud569\ub2c8\ub2e4. \uc601\uc0c1\uc774\ub098 \uc74c\uc131 \ubc0f \uc790\uc5f0\uc5b4\ub85c \uad6c\uc131\ub41c \ube44\uc815\ud615 \ub370\uc774\ud130\ub97c \uc785\ub825\uc73c\ub85c \ubc1b\uc544\ub4e4\uc5ec \uc758\uc0ac\uacb0\uc815\uc744 \ub0b4\ub9b4 \uc218 \uc788\ub3c4\ub85d \uc720\uc6a9\ud55c \uc815\ubcf4\ub97c \ucd94\ucd9c\ud558\uae30 \uc704\ud574\uc11c \ub2e4\uc74c\uacfc \uac19\uc740 \ubb38\uc81c\ub97c \ud568\uaed8 \ud480 \uc218 \uc788\ub294 \ubd84\ub4e4\uc744 \ucc3e\uace0 \uc788\uc2b5\ub2c8\ub2e4. (\uc2a4\ucffc\ub4dc \uc778\ud130\ubdf0). \ubaa8\ubc14\uc77c \ud658\uacbd\uc5d0\uc11c \ube60\ub978 \uc18d\ub3c4\ub97c \ub0bc \uc218 \uc788\ub294 \uacbd\ub7c9 \ubaa8\ub378\uacfc \ucd5c\uc801\ud654\uc5d0 \ub300\ud55c \ubb38\uc81c \ud6a8\uc728\uc801\uc774\uace0 label\uc758 \uc911\uc694\ub3c4\ub97c \uc870\uc808\ud560 \uc218 \uc788\ub294 multi-task \ud639\uc740 multi-label \ubaa8\ub378\uc5d0 \ub300\ud55c \ubb38\uc81c Partial multi-modal \ub370\uc774\ud130\ub97c \ud65c\uc6a9\ud558\ub294 \ubb38\uc81c \uc2a4\ud2b8\ub9bc\uc73c\ub85c \uc720\uc785\ub418\ub294 \uc0ac\uc6a9\uc790 \ud589\ub3d9 \ub85c\uadf8\uc640 content understanding \uacb0\uacfc\uc5d0 \uae30\ubc18\ud55c \uc2e4\uc2dc\uac04 \uc774\uc0c1 \uc0ac\uc6a9\uc790(ex. \uc2a4\ud338/\uac00\uc9dc \uacc4\uc815)\ub97c \ud0d0\uc9c0\ud558\ub294 \ubb38\uc81c Active learning\uc744 \ud1b5\ud55c \ud6a8\uc728\uc801\uc778 \ub370\uc774\ud130 \ub77c\ubca8\ub9c1 \ubc29\ubc95 \ud639\uc740 \ubaa8\ub378 \ud559\uc2b5\uc5d0 \ud544\uc694\ud55c \ub370\uc774\ud130\ub97c \uc904\uc77c \uc218 \uc788\ub294 core-set selection \ubc29\ubc95 [Generative AI] \ub2e4\uc591\ud55c \uc0dd\uc131\ud615 AI \uc5f0\uad6c \uac1c\ubc1c\uc744 \ud1b5\ud574 \uc0ac\uc6a9\uc790\ub4e4\uc5d0\uac8c \uc774\uc804\uc5d0 \uc5c6\ub358 \uc0c8\ub85c\uc6b4 \uacbd\ud5d8\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc11c\ube44\uc2a4 \ub0b4\uc5d0\uc11c \uc0ac\uc6a9\uc790\ub4e4\uc774 \uac1c\uc778\ud654\ub41c \ucee8\ud150\uce20\ub97c \uc27d\uac8c \uc0dd\uc131\ud558\uace0 \uc790\uae30 \ud45c\ud604\uc744 \ud560 \uc218 \uc788\ub294 \ub3c4\uad6c\ub97c \ub9cc\ub4e4\uba70, \uc0dd\uc131\ud615 AI\ub97c \ud65c\uc6a9\ud574 \uc0c8\ub85c\uc6b4 \uae30\ub2a5\uc744 \uac1c\ubc1c\ud569\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 \ud568\uaed8 \ub2e4\uc74c\uacfc \uac19\uc740 \ubb38\uc81c\ub97c \ud574\uacb0\ud560 \uc218 \uc788\ub294 \ubd84\ub4e4\uc744 \ucc3e\uace0 \uc788\uc2b5\ub2c8\ub2e4. (\uc2a4\ucffc\ub4dc \uc778\ud130\ubdf0). \uc0ac\uc6a9\uc790\uac00 \uc6d0\ud558\ub294 \ub300\uc0c1\uc758 \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\ub294 \uac1c\uc778\ud654\ub41c \uc774\ubbf8\uc9c0 \uc0dd\uc131 \ubaa8\ub378 \uac1c\ubc1c \ub300\uaddc\ubaa8 \uc5b8\uc5b4\ubaa8\ub378(Large Language Model)\uc744 \ud65c\uc6a9\ud55c \uc0c8\ub85c\uc6b4 \ud53c\uccd0 \uac1c\ubc1c, \uc774\ub97c \uc704\ud55c \ub300\uaddc\ubaa8 \uc5b8\uc5b4 \ubaa8\ub378 \ud559\uc2b5, \ud29c\ub2dd \ubc0f \uc11c\ube59 \ud070 \uaddc\ubaa8\uc758 \uc0dd\uc131\ud615 \ubaa8\ub378\uc774 \ub300\uc6a9\ub7c9 \ud2b8\ub798\ud53d\uc744 \uc548\uc815\uc801\uc73c\ub85c \ucc98\ub9ac\ud560 \uc218 \uc788\ub3c4\ub85d \ubaa8\ub378 \uac1c\ubc1c \ubc0f \ucd5c\uc801\ud654 \uc0dd\uc131\ud615 \ubaa8\ub378\uc744 \ud65c\uc6a9\ud558\uc5ec \uc11c\ube44\uc2a4 \ub0b4 \uc0ac\uc6a9\uc790 \uacbd\ud5d8\uc744 \ud601\uc2e0\ud560 \uc218 \uc788\ub294 \ubc29\ubc95\uc5d0 \ub300\ud55c \uc5f0\uad6c\uc640 \uace0\ubbfc [\uacf5\ud1b5] \uacf5\ud1b5\uc801\uc73c\ub85c, \uc81c\ud488\uc5d0 \ud3ec\ud568\ub418\ub294 AI \uae30\uc220\uc744 \uc5f0\uad6c\ud558\uae30 \uc704\ud55c \ub178\ub825\ub4e4\ub3c4 \uafb8\uc900\ud788 \uc9c4\ud589\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc2e4\uc81c \ud504\ub85c\ub355\uc158 \ud658\uacbd\uc5d0\uc11c\ub294 Kaggle\uacfc \uac19\uc740 \uc815\uc81c\ub41c \ub370\uc774\ud130\uc14b\uc774 \uc874\uc7ac\ud558\uc9c0 \uc54a\uc73c\uba70, \ub300\ubd80\ubd84\uc758 \uacbd\uc6b0 \ub9e4\uc77c \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\uac00 \uc2dc\uc2a4\ud15c\uc5d0 \uc720\uc785\ub429\ub2c8\ub2e4. \uc5b4\uc81c\ubcf4\ub2e4 \uc624\ub298 \ub354 \ub098\uc740 \ubaa8\ub378\uc744 \uc790\ub3d9\uc73c\ub85c \uc0dd\uc131\ud558\ub294 Flywheel\uc744 \uad6c\ucd95\ud558\uae30 \uc704\ud574, \ud568\uaed8 \ub2e4\uc74c\uacfc \uac19\uc740 \ubb38\uc81c\ub97c \ud574\uacb0\ud560 \uc218 \uc788\ub294 \ubd84\ub4e4\uc744 \ucc3e\uace0 \uc788\uc2b5\ub2c8\ub2e4. Highly imbalanced \ub610\ub294 noisy label \ub370\uc774\ud130\ub97c \ub2e4\ub8e8\ub294 \ubc29\ubc95 \uae30\uc874\uc5d0 deploy\ub41c \ubaa8\ub378\uc744 \uc9c0\uc18d\uc801\ud574\uc11c \uac1c\uc120\ud560 \uc218 \uc788\ub294 continual/life-long learning \ubc29\ubc95 \ubaa8\ub378 task \uc694\uad6c\uc0ac\ud56d\uc758 \ubcc0\ud654\uc640 \uc2e0\uaddc \uc11c\ube44\uc2a4\uc5d0 \ub300\uc751\ud560 \uc218 \uc788\ub294 meta-learning \ubc29\ubc95 Large scale model\uc744 \ud559\uc2b5\ud558\uace0, \uc2e4\uc81c \uc11c\ube44\uc2a4 \ud658\uacbd\uc5d0\uc11c \ucd08\ub2f9 \uc218\ubc31 \ub610\ub294 \uc218\ucc9c \uac1c\uc758 \uc785\ub825\uc744 \uc548\uc815\uc801\uc73c\ub85c \ucc98\ub9ac\ud560 \uc218 \uc788\ub3c4\ub85d \ud560 \uc218 \uc788\ub294 modeling, optimization, distillation \ubc29\ubc95 \uc9c0\uc6d0 \uc790\uaca9 AI/ML\uc5d0 \ub300\ud55c \uae30\ubcf8 \uc9c0\uc2dd\uacfc \uc801\uc5b4\ub3c4 \ud55c \uac1c \uc774\uc0c1\uc758 \ud2b9\uc815 \ub3c4\uba54\uc778\uc5d0 \ub300\ud55c \uae4a\uc774 \uc788\ub294 \uc9c0\uc2dd\uc744 \uac16\ucd94\uace0, \uad00\ub828 \ud504\ub85c\uc81d\ud2b8 \uacbd\ud5d8\uc774 \uc788\uc73c\uc2e0 \ubd84 AI \uae30\uc220\uc758 \uc11c\ube44\uc2a4\ud654\uc5d0 \uad00\uc2ec\uc774 \ub9ce\uc73c\uc2e0 \ubd84 Tensorflow, PyTorch, CatBoost, JAX \ub4f1 \uc624\ud508\uc18c\uc2a4 \ud504\ub808\uc784\uc6cc\ud06c \uae30\ubc18 \uac1c\ubc1c\uc5ed\ub7c9\uc744 \ud3ec\ud568\ud55c \ud30c\uc774\uc36c \uac1c\ubc1c\uc5ed\ub7c9\uc774 \ucda9\ubd84\ud558\uc2e0 \ubd84 ML \ubaa8\ub378\uc744 \ud559\uc2b5\ud558\uace0 \uc11c\ube44\uc2a4\uc5d0 \ubc30\ud3ec\ud558\uae30\uae4c\uc9c0 \ud544\uc694\ud55c \uc5d4\uc9c0\ub2c8\uc5b4\ub9c1 \uc5ed\ub7c9\uc744 \uac16\ucd94\uc2e0 \ubd84 Exploratory Data Analysis(EDA)\ub97c \ud1b5\ud574, \ub370\uc774\ud130\uc758 \ud1b5\uacc4\uc801 \ud2b9\uc131\uacfc \ud328\ud134\uc744 \ubc1c\uacac\ud558\uace0 \uc774\ub97c ML \ubaa8\ub378\uc5d0 \ubc18\uc601\ud558\uc2e4 \uc218 \uc788\ub294 \ubd84 \uad6c\ud604\uccb4\uac00 \uacf5\uac1c\ub418\uc9c0 \uc54a\uc740 \ub17c\ubb38\uc744 \uc77d\uace0 \uc774\ub97c \ube60\ub974\uace0 \uc815\ud655\ud558\uac8c \uad6c\ud604\ud560 \uc218 \uc788\ub294 \ub2a5\ub825\uc744 \uac16\ucd94\uc2e0 \ubd84 \uacf5\uac1c\ub41c \ubca4\uce58\ub9c8\ud06c \ub370\uc774\ud130 \uc14b\uc744 \uc774\uc6a9\ud574 \ubaa8\ub378\uc758 \ud14c\uc2a4\ud2b8 \uc131\ub2a5\uc744 \uc62c\ub9ac\uae30 \uc704\ud574 \uc5ec\ub7ec \uac00\uc9c0 \ubaa8\ub378\ub9c1\uc744 \ud574\ubcf8 \uacbd\ud5d8\uc774 \uc788\uc73c\uc2e0 \ubd84 \ud559\uc704\ub098 \uad6d\uc801\uc740 \ubb34\uad00\ud569\ub2c8\ub2e4. \uc6b0\ub300 \uc0ac\ud56d \uc2e4\uc81c \uc11c\ube44\uc2a4\uc5d0 AI \uae30\uc220\uc744 \ud1b5\ud569\ud558\uace0 \uc8fc\uc694 \uc9c0\ud45c\ub97c \uc720\uc758\ubbf8\ud558\uac8c \ud5a5\uc0c1\uc2dc\ucf1c \ubcf8 \uacbd\ud5d8\uc774 \uc788\uc73c\uc2e0 \ubd84 \uae30\uacc4\ud559\uc2b5 \uad00\ub828 \ud559\ud68c \ubc0f \uc800\ub110 \uac8c\uc7ac \uc2e4\uc801 \ud639\uc740 AI \uad00\ub828 \ub300\ud68c \uc218\uc0c1 \uc2e4\uc801\uc774 \uc788\uc73c\uc2e0 \ubd84 \ud074\ub77c\uc774\uc5b8\ud2b8(Android, iOS) \ud639\uc740 \ubc31\uc5d4\ub4dc \uac1c\ubc1c \uacbd\ud5d8\uc774 \uc788\uc73c\uc2e0 \ubd84 \uae30\uacc4\ud559\uc2b5 \uad00\ub828 \uc624\ud508 \uc18c\uc2a4 \uac1c\ubc1c\uc5d0 \ucc38\uc5ec\ud574 \ubcf8 \uacbd\ud5d8\uc774 \uc788\uc73c\uc2e0 \ubd84 AI/ML \ub3c4\uba54\uc778 \uc804\ubc18\uc5d0 \ub300\ud55c \uc774\ud574\ub97c \uac16\ucd94\uc2e0 \ubd84 A/B \ud14c\uc2a4\ud2b8 \uc2e4\ud5d8 \uae30\ud68d \ubc0f \ud0c0\uac9f KPI \uc9c0\ud45c\ub97c \uc815\uc758\ud558\uace0, SQL\uae30\ubc18 \ub370\uc774\ud130 \ubd84\uc11d\uc744 \uc9c4\ud589\ud55c \uacbd\ud5d8\uc774 \uc788\uc73c\uc2e0 \ubd84 \uc601\uc5b4\uc5d0 \ub2a5\ud1b5\ud558\uc2e0 \ubd84 $ - $ a year None \ud558\uc774\ud37c\ucee4\ub125\ud2b8\uac00 \ucc44\uc6a9\ud558\ub294 \ud3ec\uc9c0\uc158\uc5d0 \uc9c0\uc6d0\ud558\ub294 \uacbd\uc6b0, \uac1c\uc778\uc815\ubcf4 \ucc98\ub9ac\uc5d0 \uad00\ud558\uc5ec\uc11c\ub294 \ubcf8 \uac1c\uc778\uc815\ubcf4\ucc98\ub9ac\ubc29\uce68\uc774 \uc801\uc6a9\ub429\ub2c8\ub2e4: https://career.hyperconnect.com/privacy", "stars": 0.0, "medallionProgram": null, "location_flexibility": null, "work_location_option": "onsite", "canonicalPositionUrl": "https://join.matchgroupcareers.com/careers/job/22203760", "isPrivate": false}], "debug": {}, "count": 15, "countFilterByMatchScore": null, "location_user": "any", "location_used": "", "locationInsights": null, "showWizard": 1, "disableLocationSearchDropdown": false, "pcsAutocompleteLocationEnabled": 0, "recommended_star_threshold": 4.0, "mocTitle": null, "fuzzyResultsReturned": false, "query": {"query": "trust", "location": "", "department": [], "skill": [], "seniority": [], "pid": "7389121"}, "userTitles": [], "isThinProfile": false, "veteranProgramDetails": [], "enableTargetedResume": 0, "jobCardConfig": null, "facetDisplaySort": {"skills": "count_desc", "departments": "count_desc", "seniority": "count_desc", "locations": "count_desc"}, "facets": {"skills": {"Python": 23, "Teamwork": 20, "Product Management": 17, "Communication": 17, "Engineering": 16, "Problem Solving": 14, "SQL": 13, "Jupyter Notebook": 10, "AWS Quicksight": 10, "Fraud Detection": 10, "Safety": 10, "Trust": 10, "Fraud": 10, "Risk": 10, "Aws RDS": 10, "AWS Redshift": 10, "Grafana": 10, "Databricks": 10, "AWS Firehose": 10, "Amazon Kinesis": 10}, "departments": {"Hyperconnect": 4, "Tinder": 4, "Hinge": 3, "Match Group": 3, "Affinity": 1}, "seniority": {"Mid/Senior": 13, "Entry": 1, "Director": 1}, "locations": {"Seoul": 4, "New York, New York": 3, "Los Angeles, California": 2, "West Hollywood, California": 2, "Dallas, Texas": 1, "San Francisco, California": 1, "Tokyo, Japan": 1, "United States": 1}}, "isSubQuery": true, "showBusinessUnitInPositionSidebar": false, "hideDepartment": null, "iframeImplementation": null, "pcsTextConfiguration": {}, "facetNoTitlecase": null, "skipCSSFacetCapitalization": false, "enableMapClustering": false, "readmoreInstructionEnabled": false, "candidateLogin": {"postApplyReviewLink": {"enabled": true, "url": "/api/apply/v2/profile/review/initialize_user?domain=gotinder.com", "title": "What's next? 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